| Cloudflare | 60.0 | 68 | 0 |
| IBM | 51.2 | 36 | 0 |
| Tesla | 45.6 | 24 | 0 |
| Microsoft | 40.1 | 16 | 0 |
| Vertiv | 40.0 | 0 | 23 |
| Data Analytics for Machine Learning | 34.1 | 5 | 1 |
| Azure DevOps Server | 29.5 | 7 | 0 |
| Q18698690 | 29.5 | 7 | 0 |
| Equinix | 29.4 | 2 | 2 |
| deep learning super sampling | 29.4 | 2 | 2 |
| Hugging Face | 28.4 | 3 | 1 |
| Microsoft SQL Server | 27.6 | 6 | 0 |
| OVHcloud | 27.6 | 6 | 0 |
| Oracle CRM | 27.6 | 6 | 0 |
| Oracle Fusion Applications | 27.6 | 6 | 0 |
| Tesla Cybertruck | 27.6 | 6 | 0 |
| Oracle Database | 25.4 | 5 | 0 |
| Media Creation Tool | 25.4 | 5 | 0 |
| Azure | 24.3 | 2 | 1 |
| bidirectional encoder representations from transformers | 24.3 | 2 | 1 |
| Dolly | 24.3 | 2 | 1 |
| NVIDIA Project DIGITS | 24.3 | 2 | 1 |
| Q80689 | 22.8 | 4 | 0 |
| Oracle E-Business Suite | 22.8 | 4 | 0 |
| Tesla Model 3 | 22.8 | 4 | 0 |
| Microsoft Lumia 640 XL | 22.8 | 4 | 0 |
| Tesla Model Y | 22.8 | 4 | 0 |
| Microsoft Windows | 19.6 | 3 | 0 |
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| Datalogix | 19.6 | 3 | 0 |
| IBM Bluemix | 19.6 | 3 | 0 |
| Microsoft Lumia 640 | 19.6 | 3 | 0 |
| Dataiku | 19.6 | 3 | 0 |
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| Oracle Cloud Platform | 19.6 | 3 | 0 |
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| Microsoft Learn | 19.6 | 3 | 0 |
| Microsoft Typography | 19.6 | 3 | 0 |
| System Center Operations Manager | 18.5 | 1 | 1 |
| Microsoft 365 | 18.5 | 1 | 1 |
| Oracle Enterprise Manager Ops Center | 18.5 | 1 | 1 |
| Ryota Tomioka | 18.5 | 1 | 1 |
| Cerebras | 18.5 | 1 | 1 |
| Road Roughness Estimation Using Machine Learning | 18.5 | 1 | 1 |
| Machine Learning and Deep Learning -- A review for Ecologists | 18.5 | 1 | 1 |
| NVIDIA A800 40GB Active GPU | 18.5 | 1 | 1 |
| Lambda Labs | 18.5 | 1 | 1 |
| Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023 | 18.5 | 1 | 1 |
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| Q135647703 | 18.5 | 1 | 1 |
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| AI accelerator | 17.4 | 0 | 3 |
| Tokens, Watts, and Geography: AI Infrastructure | 17.4 | 0 | 3 |
| Q11219 | 15.6 | 2 | 0 |
| Q11278 | 15.6 | 2 | 0 |
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| DirectX | 15.6 | 2 | 0 |
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| Microsoft Dynamics NAV | 15.6 | 2 | 0 |
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| IBM Informix | 15.6 | 2 | 0 |
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| EDGAR | 15.6 | 2 | 0 |
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| Oracle Property Manager | 15.6 | 2 | 0 |
| Verari Technologies | 15.6 | 2 | 0 |
| Q10984556 | 15.6 | 2 | 0 |
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| Microsoft Lumia 950 XL | 15.6 | 2 | 0 |
| Windows Subsystem for Linux | 15.6 | 2 | 0 |
| Microsoft Entra ID | 15.6 | 2 | 0 |
| Azure Cognitive Search | 15.6 | 2 | 0 |
| Microsoft Dynamics 365 | 15.6 | 2 | 0 |
| Oracle Cloud | 15.6 | 2 | 0 |
| CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows | 15.6 | 2 | 0 |
| Microsoft Academic Graph | 15.6 | 2 | 0 |
| Microsoft Saudi | 15.6 | 2 | 0 |
| NASDAQ/Ngs (Global Select Market) | 15.6 | 2 | 0 |
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| SPARQL Generation: an analysis on fine-tuning OpenLLaMA for Question Answering over a Life Science Knowledge Graph | 15.6 | 2 | 0 |
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| Hugging Face Hub | 15.6 | 2 | 0 |
| Nvidia RTX Pro 6000 Blackwell Workstation Edition | 15.6 | 2 | 0 |
| Microsoft Security Copilot | 15.6 | 2 | 0 |
| data center | 13.8 | 0 | 2 |
| queries per second | 13.8 | 0 | 2 |
| National Oceanographic Data Center | 13.8 | 0 | 2 |
| Category:Data centers | 13.8 | 0 | 2 |
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| H2O | 13.8 | 0 | 2 |
| Expedient | 13.8 | 0 | 2 |
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| Illumio | 13.8 | 0 | 2 |
| Low latency and efficient optical flow control for intra data center networks | 13.8 | 0 | 2 |
| High throughput optimization of stem cell microenvironments. | 13.8 | 0 | 2 |
| Using Machine Learning Methods to Predict Experimental High Throughput Screening Data | 13.8 | 0 | 2 |
| Save energy and $$$ in the data center. The cabling installed today has to meet the requirements of the cabling for tomorrow. Most data centers are planning for speeds of 10-Gigabit Ethernet. | 13.8 | 0 | 2 |
| Graph algorithms for machine learning: a case-control study based on prostate cancer populations and high throughput transcriptomic data | 13.8 | 0 | 2 |
| RFC 8257: Data Center TCP (DCTCP): TCP Congestion Control for Data Centers | 13.8 | 0 | 2 |
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| HT-Paxos: high throughput state-machine replication protocol for large clustered data centers | 13.8 | 0 | 2 |
| Reconfigurable very high throughput low latency VLSI (FPGA) design architecture of CRC 32 | 13.8 | 0 | 2 |
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| A two-time-scale load balancing framework for minimizing electricity bills of Internet Data Centers | 13.8 | 0 | 2 |
| Minimizing Electricity Bills for Geographically Distributed Data Centers with Renewable and Cooling Aware Load Balancing | 13.8 | 0 | 2 |
| Mirror, Mirror in the Data Center: Remote Data Copy For Disaster Recovery | 13.8 | 0 | 2 |
| Machine Learning Aided Scheme for Load Balancing in Dense IoT Networks | 13.8 | 0 | 2 |
| Design of High Throughput and Cost-Efficient Data Center Networks | 13.8 | 0 | 2 |
| Agent-based load balancing in Cloud data centers | 13.8 | 0 | 2 |
| Machine Learning with Sensitivity Analysis to Determine Key Factors Contributing to Energy Consumption in Cloud Data Centers | 13.8 | 0 | 2 |
| Optimal Load Balancing and Energy Cost Management for Internet Data Centers in Deregulated Electricity Markets | 13.8 | 0 | 2 |
| data center management | 13.8 | 0 | 2 |
| Structured illumination microscopy combined with machine learning enables the high throughput analysis and classification of virus structure | 13.8 | 0 | 2 |
| Power-Aware Multi-data Center Management Using Machine Learning | 13.8 | 0 | 2 |
| Towards energy-aware scheduling in data centers using machine learning | 13.8 | 0 | 2 |
| A data center network featuring low latency and energy efficiency based on all optical core interconnect | 13.8 | 0 | 2 |
| Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions | 13.8 | 0 | 2 |
| Machine Learning and Knowledge Extraction | 13.8 | 0 | 2 |
| Uncertainty-Quantified Hybrid Machine Learning/Density Functional Theory High Throughput Screening Method for Crystals | 13.8 | 0 | 2 |
| Predicted Optimal Bifunctional Electrocatalysts for the Hydrogen Evolution Reaction and the Oxygen Evolution Reaction Using Chalcogenide Heterostructures Based on Machine Learning Analysis of in Silico Quantum Mechanics Based High Throughput Screeni | 13.8 | 0 | 2 |
| A machine learning algorithm for high throughput identification of FTIR spectra: Application on microplastics collected in the Mediterranean Sea | 13.8 | 0 | 2 |
| Balaji Prabhakar | 13.8 | 0 | 2 |
| A High Throughput and Unbiased Machine Learning Approach for Classification of Graphene Dispersions | 13.8 | 0 | 2 |
| CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19 | 13.8 | 0 | 2 |
| Elucidating the constitutive relationship of calcium-silicate-hydrate gel using high throughput reactive molecular simulations and machine learning | 13.8 | 0 | 2 |
| Sabey Data Centers | 13.8 | 0 | 2 |
| Intelligence‐enabled approach for load balancing in software‐defined data center networks | 13.8 | 0 | 2 |
| Joint optimization of energy saving and load balancing for data center networks based on software defined networks | 13.8 | 0 | 2 |
| Implementation of Microsoft SQL Server using ‘AlwaysOn’ for High Availability and Disaster Recovery without Shared Storage | 13.8 | 0 | 2 |
| Building and Accelerating a Declarative Platform for Machine Learning Model Serving | 13.8 | 0 | 2 |
| ANALISA DAN DESAIN SISTEM MONITORING TRANSAKSI DATA CENTER DAN DISASTER RECOVERY CENTER STUDI KASUS PADA DIREKTORAT PENGELOLAAN INFORMASI ADMINISTRASI KEPENDUDUKAN DITJEN KEPENDUDUKAN DAN PENCATATAN SIPIL | 13.8 | 0 | 2 |
| Monitoring Urban Deprived Areas with Remote Sensing and Machine Learning in Case of Disaster Recovery | 13.8 | 0 | 2 |
| New requirements of information security protection on state grid Shanghai Municipal Electric Power Company Data Center compared with Disaster Recovery Center | 13.8 | 0 | 2 |
| Packet-Based Load Balancing in Data Center Networks | 13.8 | 0 | 2 |
| Coarse-Grained Load Balancing with Traffic-Aware Marking in Data Center Networks | 13.8 | 0 | 2 |
| Traffic Load Balancing Schemes for Devolved Controllers in Mega Data Centers | 13.8 | 0 | 2 |
| RMC: Reordering Marking and Coding for Fine-Grained Load Balancing in Data Centers | 13.8 | 0 | 2 |
| Machine learning methodology for high throughput personalized neutron dose reconstruction in mixed neutron + photon exposures | 13.8 | 0 | 2 |
| DataBank | 13.8 | 0 | 2 |
| Disaster recovery of the INFN Tier–1 data center: lesson learned | 13.8 | 0 | 2 |
| Determine a load balancing mechanism for allocation of shared resources in a storage system by training a machine learning module based on number of I | 13.8 | 0 | 2 |
| Determine a load balancing mechanism for allocation of shared resources in a storage system using a machine learning module based on number of I/O ope | 13.8 | 0 | 2 |
| Load Balancing Schemes for Data Center Network:Problems,Progress and Prospects | 13.8 | 0 | 2 |
| System and method to achieve virtual machine backup load balancing using machine learning | 13.8 | 0 | 2 |
| Accurate analytics, quality of service and load balancing for internet protocol fragmented packets in data center fabrics | 13.8 | 0 | 2 |
| Shared prediction engine for machine learning model deployment | 13.8 | 0 | 2 |
| Optimizing database migration in high availability and disaster recovery computing environments | 13.8 | 0 | 2 |
| Load balancing between edge systems in a high availability edge system pair | 13.8 | 0 | 2 |
| Architecture for table-based mathematical operations for inference acceleration in machine learning | 13.8 | 0 | 2 |
| Data center PUE optimization based on machine learning | 13.8 | 0 | 2 |
| Dynamic load balancing and configuration management for heterogeneous compute accelerators in a data center | 13.8 | 0 | 2 |
| Data Center Coalition | 13.8 | 0 | 2 |
| Virtual Machine Consolidation for Stochastic Load Balancing in Cloud Data Center Management | 13.8 | 0 | 2 |
| The Cloud Technology Double Live Data Center Information System Research and Design Based on Disaster Recovery Platform | 13.8 | 0 | 2 |
| A DSEL for high throughput and low latency software‐defined radio on multicore CPUs | 13.8 | 0 | 2 |
| Machine Learning and High Throughput Synthesis Acceleration of the Discovery of Alkaline Electrolyte Oxygen Evolution Reaction Electrocatalysts | 13.8 | 0 | 2 |
| Renewable-aware geographical load balancing of web applications for sustainable data centers | 13.8 | 0 | 2 |
| Embedding individualized machine learning prediction models for energy efficient VM consolidation within Cloud data centers | 13.8 | 0 | 2 |
| A novel software‐defined networking approach for load balancing in data center networks | 13.8 | 0 | 2 |
| Scheduling data streams for low latency and high throughput on a Cray XC40 using Libfabric | 13.8 | 0 | 2 |
| Hidden Markov Model-based Load Balancing in Data Center Networks | 13.8 | 0 | 2 |
| High throughput proteomic analysis and machine learning algorithm identifies DUOX2 (dual oxidase 2) as a novel biomarker for response prediction of concurrent chemoradiotherapy for locally advanced rectal cancer. | 13.8 | 0 | 2 |
| Distributed machine learning load balancing strategy in cloud computing services | 13.8 | 0 | 2 |
| Oil Production Monitoring using Gradient Boosting Machine Learning Algorithm | 13.8 | 0 | 2 |
| Virtual machine scheduling strategy based on machine learning algorithms for load balancing | 13.8 | 0 | 2 |
| Post-Disaster Recovery Assessment with Machine Learning-Derived Land Cover and Land Use Information | 13.8 | 0 | 2 |
| Performance tuning for machine learning-based software development effort prediction models | 13.8 | 0 | 2 |
| Low Latency and High Throughput Write-Ahead Logging Using CAPI-Flash | 13.8 | 0 | 2 |
| Cache-aware load balancing of data center applications | 13.8 | 0 | 2 |
| Deep Learning-Based Data Storage for Low Latency in Data Center Networks | 13.8 | 0 | 2 |
| Inference serving with end-to-end latency SLOs over dynamic edge networks | 13.8 | 0 | 2 |
| Resource Prediction for Big Data Processing in a Cloud Data Center : A Machine Learning Approach | 13.8 | 0 | 2 |
| Analysis of load balancing in cloud data centers | 13.8 | 0 | 2 |
| A Review on: Network Load Balancing in Dynamic Data Center | 13.8 | 0 | 2 |
| Capacity Planning for Green Data Center Sustainability | 13.8 | 0 | 2 |
| Improved Load Balancing on Software Defined Network-based Equal Cost Multipath Routing in Data Center Network | 13.8 | 0 | 2 |
| Load Balancing and Thermal-Aware in Geo-Distributed Cloud Data Centers Based on Vlans | 13.8 | 0 | 2 |
| Secure and Sustainable Load Balancing of Edge Data Centers in Fog Computing | 13.8 | 0 | 2 |
| Review of Load Balancing Mechanisms in SDN-Based Data Centers | 13.8 | 0 | 2 |
| Online Load Balancing for Distributed Control Plane in Software-Defined Data Center Network | 13.8 | 0 | 2 |
| Deferred Continuous Batching in Resource-Efficient Large Language Model Serving | 13.8 | 0 | 2 |
| Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads | 13.8 | 0 | 2 |
| Database Workload Capacity Planning using Time Series Analysis and Machine Learning | 13.8 | 0 | 2 |
| Inference Optimization of Foundation Models on AI Accelerators | 13.8 | 0 | 2 |
| vSwitchLB: Stratified Load Balancing for vSwitch Efficiency in Data Centers | 13.8 | 0 | 2 |
| Enhancing Load Balancing With In-Network Recirculation to Prevent Packet Reordering in Lossless Data Centers | 13.8 | 0 | 2 |
| Asymmetry-Aware Load Balancing With Adaptive Switching Granularity in Data Center | 13.8 | 0 | 2 |
| Low Latency, High Throughput Trade Surveillance System Using In-Memory Data Grid | 13.8 | 0 | 2 |
| Machine learning for load balancing in the Linux kernel | 13.8 | 0 | 2 |
| Load Balancing in Distributed Cloud Data Center Configurations | 13.8 | 0 | 2 |
| Cost-Efficient Serverless Inference Serving with Joint Batching and Multi-Processing | 13.8 | 0 | 2 |
| CoFRIS: Coordinated Frequency and Resource Scaling for GPU Inference Servers | 13.8 | 0 | 2 |
| Machine Learning Data Center Workloads Using Generative Adversarial Networks | 13.8 | 0 | 2 |
| CQIL: Inference latency optimization with concurrent computation of quasi-independent layers | 13.8 | 0 | 2 |
| Systems and methods for efficient scalability and high availability of applications in container orchestration cloud environment | 13.8 | 0 | 2 |
| Q136215752 | 13.8 | 0 | 2 |
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| Q136271712 | 13.8 | 0 | 2 |
| Machine learning-guided high throughput nanoparticle design | 13.8 | 0 | 2 |
| Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts | 13.8 | 0 | 2 |
| Ensemble machine learning framework for predictive operational load balancing | 13.8 | 0 | 2 |
| Q136920086 | 13.8 | 0 | 2 |
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| Orbital AI Data Center Speculation | 13.8 | 0 | 2 |
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| edge inference | 13.8 | 0 | 2 |
| opposition to AI data centers | 13.8 | 0 | 2 |
| System and method for intelligent data center power management and energy market disaster recovery | 13.8 | 0 | 2 |
| Java | 9.8 | 1 | 0 |
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| Tesla Model X | 9.8 | 1 | 0 |
| Nokia Asha series | 9.8 | 1 | 0 |
| Microsoft Excel Viewer | 9.8 | 1 | 0 |
| IBM Canada | 9.8 | 1 | 0 |
| Windows Automated Installation Kit | 9.8 | 1 | 0 |
| Microsoft App-V | 9.8 | 1 | 0 |
| System Center Mobile Device Manager | 9.8 | 1 | 0 |
| Oracle Cloud File System | 9.8 | 1 | 0 |
| The Java Language Specification | 9.8 | 1 | 0 |
| Java Media Framework | 9.8 | 1 | 0 |
| Outlook on the web | 9.8 | 1 | 0 |
| IBM Lotus Approach | 9.8 | 1 | 0 |
| Storage Technology Corporation | 9.8 | 1 | 0 |
| Microsoft Songsmith | 9.8 | 1 | 0 |
| SQL Server Compact | 9.8 | 1 | 0 |
| Oracle Application Development Framework | 9.8 | 1 | 0 |
| Microsoft MapPoint | 9.8 | 1 | 0 |
| Q1854343 | 9.8 | 1 | 0 |
| IBM Lotus Expeditor | 9.8 | 1 | 0 |
| IBM Lotus Freelance Graphics | 9.8 | 1 | 0 |
| IBM Lotus Word Pro | 9.8 | 1 | 0 |
| MS Sans Serif | 9.8 | 1 | 0 |
| Sylfaen | 9.8 | 1 | 0 |
| PeopleSoft | 9.8 | 1 | 0 |
| Microsoft Robotics Developer Studio | 9.8 | 1 | 0 |
| Microsoft WebMatrix | 9.8 | 1 | 0 |
| Microsoft Flight Simulator X | 9.8 | 1 | 0 |
| Nokia Suite | 9.8 | 1 | 0 |
| Oracle Call Interface | 9.8 | 1 | 0 |
| System Center Data Protection Manager | 9.8 | 1 | 0 |
| Microsoft Analysis Services | 9.8 | 1 | 0 |
| Microsoft Entourage | 9.8 | 1 | 0 |
| Primavera | 9.8 | 1 | 0 |
| Rational Synergy | 9.8 | 1 | 0 |
| IBM Z | 9.8 | 1 | 0 |
| Windows Hardware Lab Kit | 9.8 | 1 | 0 |
| Tuxedo | 9.8 | 1 | 0 |
| Microsoft Security Development Lifecycle | 9.8 | 1 | 0 |
| JavaFX | 9.8 | 1 | 0 |
| Microsoft Flight | 9.8 | 1 | 0 |
| Microsoft Forefront | 9.8 | 1 | 0 |
| Microsoft Research Image Composite Editor | 9.8 | 1 | 0 |
| IBM General Parallel File System | 9.8 | 1 | 0 |
| JD Edwards | 9.8 | 1 | 0 |
| Microsoft Agent | 9.8 | 1 | 0 |
| UNIX System Services | 9.8 | 1 | 0 |
| OpenWindows | 9.8 | 1 | 0 |
| IBM WebSphere | 9.8 | 1 | 0 |
| Q2554872 | 9.8 | 1 | 0 |
| Q2567249 | 9.8 | 1 | 0 |
| Apple Productivity Experience Group | 9.8 | 1 | 0 |
| Windows SteadyState | 9.8 | 1 | 0 |
| Oracle Developer Studio | 9.8 | 1 | 0 |
| Windows Vista Starter | 9.8 | 1 | 0 |
| Windows XP Media Center Edition | 9.8 | 1 | 0 |
| Oracle Grid Engine | 9.8 | 1 | 0 |
| Windows HPC Server 2008 | 9.8 | 1 | 0 |
| Agile Software Corporation | 9.8 | 1 | 0 |
| Cloudera | 9.8 | 1 | 0 |
| Windows XP Professional | 9.8 | 1 | 0 |
| Dedibox | 9.8 | 1 | 0 |
| Office Genuine Advantage | 9.8 | 1 | 0 |
| Windows Product Activation | 9.8 | 1 | 0 |
| Windows Enhanced Metafile | 9.8 | 1 | 0 |
| HP Software Division | 9.8 | 1 | 0 |
| Windows Embedded Industry | 9.8 | 1 | 0 |
| PlayReady | 9.8 | 1 | 0 |
| Microsoft Forefront Unified Access Gateway | 9.8 | 1 | 0 |
| Microsoft Office PerformancePoint Server | 9.8 | 1 | 0 |
| Sun Java Communications Suite | 9.8 | 1 | 0 |
| Java Platform | 9.8 | 1 | 0 |
| PowerVM | 9.8 | 1 | 0 |
| IBM Rational Software Modeler | 9.8 | 1 | 0 |
| Oracle Spatial | 9.8 | 1 | 0 |
| Q3569296 | 9.8 | 1 | 0 |
| MIX | 9.8 | 1 | 0 |
| Microsoft Customer Care Framework | 9.8 | 1 | 0 |
| Project Sylpheed | 9.8 | 1 | 0 |
| Microsoft .NET | 9.8 | 1 | 0 |
| Microsoft Streets & Trips | 9.8 | 1 | 0 |
| Microsoft Voice Command | 9.8 | 1 | 0 |
| Oracle Applications | 9.8 | 1 | 0 |
| TimesTen | 9.8 | 1 | 0 |
| BootVis | 9.8 | 1 | 0 |
| Oracle Exadata | 9.8 | 1 | 0 |
| Oracle Exalogic | 9.8 | 1 | 0 |
| JD Edwards EnterpriseOne | 9.8 | 1 | 0 |
| Jinitiator | 9.8 | 1 | 0 |
| Oracle VM Server for SPARC | 9.8 | 1 | 0 |
| Microsoft Active Accessibility | 9.8 | 1 | 0 |
| Microsoft Keyboard Layout Creator | 9.8 | 1 | 0 |
| Microsoft Software Licensing and Protection Services | 9.8 | 1 | 0 |
| Nvidia System Tools | 9.8 | 1 | 0 |
| OptiX | 9.8 | 1 | 0 |
| Oracle OpenWorld | 9.8 | 1 | 0 |
| ProLiant | 9.8 | 1 | 0 |
| Oracle Rdb | 9.8 | 1 | 0 |
| SQL Server Management Studio | 9.8 | 1 | 0 |
| Solaris Cluster | 9.8 | 1 | 0 |
| WebSphere Portal | 9.8 | 1 | 0 |
| Windows CE 4.0 | 9.8 | 1 | 0 |
| Allora & Calzadilla | 9.8 | 1 | 0 |
| Zune Software | 9.8 | 1 | 0 |
| Windows Easy Transfer | 9.8 | 1 | 0 |
| Microsoft IME | 9.8 | 1 | 0 |
| Analyst's Notebook | 9.8 | 1 | 0 |
| Atlantic.net | 9.8 | 1 | 0 |
| Attack Surface Analyzer | 9.8 | 1 | 0 |
| User Mode Driver Framework | 9.8 | 1 | 0 |
| Windows Phone | 9.8 | 1 | 0 |
| BigMachines | 9.8 | 1 | 0 |
| COM Structured Storage | 9.8 | 1 | 0 |
| Creative Writer 2 | 9.8 | 1 | 0 |
| Creature House Expression | 9.8 | 1 | 0 |
| CrowdTwist | 9.8 | 1 | 0 |
| Develothon | 9.8 | 1 | 0 |
| Digital Ocean | 9.8 | 1 | 0 |
| Domo | 9.8 | 1 | 0 |
| Windows shell | 9.8 | 1 | 0 |
| Emptoris | 9.8 | 1 | 0 |
| Extreme Blue | 9.8 | 1 | 0 |
| FatWire | 9.8 | 1 | 0 |
| Forefront Identity Manager | 9.8 | 1 | 0 |
| Form 10-12B | 9.8 | 1 | 0 |
| Form F-6 | 9.8 | 1 | 0 |
| Hyperion Planning | 9.8 | 1 | 0 |
| IBM Academy of Technology | 9.8 | 1 | 0 |
| IBM Check Processing Control System | 9.8 | 1 | 0 |
| IBM India Private Limited | 9.8 | 1 | 0 |
| IBM Lotus Web Content Management | 9.8 | 1 | 0 |
| IBM PureQuery | 9.8 | 1 | 0 |
| IBM Israel | 9.8 | 1 | 0 |
| IBM Rational Rose XDE | 9.8 | 1 | 0 |
| IBM Rochester | 9.8 | 1 | 0 |
| IBM Scale-out File Services | 9.8 | 1 | 0 |
| IBM Unica NetInsight | 9.8 | 1 | 0 |
| IBM WebSphere Application Server Community Edition | 9.8 | 1 | 0 |
| IBM jStart | 9.8 | 1 | 0 |
| iSeries QSHELL | 9.8 | 1 | 0 |
| IntelliPoint | 9.8 | 1 | 0 |
| Palacio de los Condes de San Mateo de Valparaiso | 9.8 | 1 | 0 |
| Q6116271 | 9.8 | 1 | 0 |
| Windows Activation Technology | 9.8 | 1 | 0 |
| Light Reading | 9.8 | 1 | 0 |
| Linux Technology Center | 9.8 | 1 | 0 |
| Microsoft Algeria | 9.8 | 1 | 0 |
| Microsoft Amalga | 9.8 | 1 | 0 |
| Microsoft Automatic Graph Layout | 9.8 | 1 | 0 |
| Microsoft Desktop Optimization Pack | 9.8 | 1 | 0 |
| Microsoft Dynamics ERP | 9.8 | 1 | 0 |
| Microsoft Dynamics C5 | 9.8 | 1 | 0 |
| Microsoft India | 9.8 | 1 | 0 |
| Microsoft Research Asia | 9.8 | 1 | 0 |
| Microsoft Site Server | 9.8 | 1 | 0 |
| Microsoft Sync Framework | 9.8 | 1 | 0 |
| Microsoft Vizact | 9.8 | 1 | 0 |
| Microsoft Word Viewer | 9.8 | 1 | 0 |
| Nginx, Inc. | 9.8 | 1 | 0 |
| NVIDIA CUDA Compiler | 9.8 | 1 | 0 |
| Nokia Lumia 520 | 9.8 | 1 | 0 |
| Oracle BI Publisher | 9.8 | 1 | 0 |
| Oracle Beehive | 9.8 | 1 | 0 |
| Oracle BPEL Process Manager | 9.8 | 1 | 0 |
| Oracle Data Mining | 9.8 | 1 | 0 |
| Oracle Designer | 9.8 | 1 | 0 |
| Oracle Discoverer | 9.8 | 1 | 0 |
| Oracle Enterprise Pack for Eclipse | 9.8 | 1 | 0 |
| Oracle NoSQL Database | 9.8 | 1 | 0 |
| Oracle Policy Automation | 9.8 | 1 | 0 |
| Oracle VDI | 9.8 | 1 | 0 |
| Oracle Multimedia | 9.8 | 1 | 0 |
| Peter Lee | 9.8 | 1 | 0 |
| PowerVM Lx86 | 9.8 | 1 | 0 |
| Primavera Systems | 9.8 | 1 | 0 |
| pureXML | 9.8 | 1 | 0 |
| RightNow Technologies | 9.8 | 1 | 0 |
| Sniffex | 9.8 | 1 | 0 |
| Sun Java System Web Proxy Server | 9.8 | 1 | 0 |
| IBM Spectrum Symphony | 9.8 | 1 | 0 |
| Sysedit | 9.8 | 1 | 0 |
| System Center Essentials | 9.8 | 1 | 0 |
| TOA Technologies | 9.8 | 1 | 0 |
| Taleo | 9.8 | 1 | 0 |
| IBM BigFix | 9.8 | 1 | 0 |
| Windows XP Tablet PC Edition | 9.8 | 1 | 0 |
| Windows Messaging | 9.8 | 1 | 0 |
| Hsiao-Wuen Hon | 9.8 | 1 | 0 |
| Windows Media Player, version 11 | 9.8 | 1 | 0 |
| Windows Media Player, version 10 | 9.8 | 1 | 0 |
| Kernel-Mode Driver Framework | 9.8 | 1 | 0 |
| Oracle VM | 9.8 | 1 | 0 |
| Microsoft Asia-Pacific R&D Group | 9.8 | 1 | 0 |
| Enhanced Mitigation Experience Toolkit | 9.8 | 1 | 0 |
| IBM Business Consulting Services | 9.8 | 1 | 0 |
| MS Gothic | 9.8 | 1 | 0 |
| MS Mincho | 9.8 | 1 | 0 |
| Q11506206 | 9.8 | 1 | 0 |
| IBM Japan | 9.8 | 1 | 0 |
| Hewlett-Packard Japan | 9.8 | 1 | 0 |
| Microsoft Egypt | 9.8 | 1 | 0 |
| IBM Korea | 9.8 | 1 | 0 |
| Windows Embedded 8 | 9.8 | 1 | 0 |
| IBM POWER | 9.8 | 1 | 0 |
| Nokia Lumia 1020 | 9.8 | 1 | 0 |
| Nokia 105 | 9.8 | 1 | 0 |
| Microsoft Development Center Serbia | 9.8 | 1 | 0 |
| Microsoft Surface Pro 2 | 9.8 | 1 | 0 |
| Nokia Lumia 1520 | 9.8 | 1 | 0 |
| Surface 2 | 9.8 | 1 | 0 |
| Nokia Lumia 1320 | 9.8 | 1 | 0 |
| Windows Server 2012 R2 | 9.8 | 1 | 0 |
| Nokia Lumia 525 | 9.8 | 1 | 0 |
| Nokia X | 9.8 | 1 | 0 |
| IBM i | 9.8 | 1 | 0 |
| Nokia X | 9.8 | 1 | 0 |
| IBM Deutschland Mittelstand Services | 9.8 | 1 | 0 |
| Q15897235 | 9.8 | 1 | 0 |
| Microsoft Virtual PC | 9.8 | 1 | 0 |
| list of major SEC enforcement actions | 9.8 | 1 | 0 |
| IBM Denmark | 9.8 | 1 | 0 |
| Q16625776 | 9.8 | 1 | 0 |
| Nokia Fastlane | 9.8 | 1 | 0 |
| Nvidia Shadowplay | 9.8 | 1 | 0 |
| IBM FlashSystem | 9.8 | 1 | 0 |
| Sun Management Center | 9.8 | 1 | 0 |
| Tealeaf | 9.8 | 1 | 0 |
| The High Point Enterprise | 9.8 | 1 | 0 |
| Microsoft Surface Pro 3 | 9.8 | 1 | 0 |
| PhotoDNA | 9.8 | 1 | 0 |
| Oracle BPA Suite | 9.8 | 1 | 0 |
| Tacit Software | 9.8 | 1 | 0 |
| IBM cloud computing | 9.8 | 1 | 0 |
| PeopleTools | 9.8 | 1 | 0 |
| Giga Nevada | 9.8 | 1 | 0 |
| Nokia X2 | 9.8 | 1 | 0 |
| Nokia X family | 9.8 | 1 | 0 |
| Surface | 9.8 | 1 | 0 |
| Duke | 9.8 | 1 | 0 |
| Databricks | 9.8 | 1 | 0 |
| machine learning | 8.7 | 0 | 1 |
| David Haussler | 8.7 | 0 | 1 |
| Q92894 | 8.7 | 0 | 1 |
| Corinna Cortes | 8.7 | 0 | 1 |
| Donald Michie | 8.7 | 0 | 1 |
| Yoav Freund | 8.7 | 0 | 1 |
| Leslie Valiant | 8.7 | 0 | 1 |
| Weka | 8.7 | 0 | 1 |
| emerging technology | 8.7 | 0 | 1 |
| lazy learning | 8.7 | 0 | 1 |
| explanation-based learning | 8.7 | 0 | 1 |
| Round-robin DNS | 8.7 | 0 | 1 |
| National Climatic Data Center | 8.7 | 0 | 1 |
| artificial neural network | 8.7 | 0 | 1 |
| deep learning | 8.7 | 0 | 1 |
| ensemble learning | 8.7 | 0 | 1 |
| Jensen Huang | 8.7 | 0 | 1 |
| supervised learning | 8.7 | 0 | 1 |
| Acronis | 8.7 | 0 | 1 |
| pattern recognition | 8.7 | 0 | 1 |
| John Hopfield | 8.7 | 0 | 1 |
| feature selection | 8.7 | 0 | 1 |
| probably approximately correct learning | 8.7 | 0 | 1 |
| boosting | 8.7 | 0 | 1 |
| Swiss Fort Knox | 8.7 | 0 | 1 |
| Colt Technology Services | 8.7 | 0 | 1 |
| load balancing | 8.7 | 0 | 1 |
| Delta rule | 8.7 | 0 | 1 |
| Knowledge Engineering and Machine Learning Group | 8.7 | 0 | 1 |
| high-throughput screening | 8.7 | 0 | 1 |
| XMM-Newton | 8.7 | 0 | 1 |
| colocation centre | 8.7 | 0 | 1 |
| Amazon Mechanical Turk | 8.7 | 0 | 1 |
| backpropagation | 8.7 | 0 | 1 |
| bootstrap aggregating | 8.7 | 0 | 1 |
| reinforcement learning | 8.7 | 0 | 1 |
| intelligent control | 8.7 | 0 | 1 |
| computer cooling | 8.7 | 0 | 1 |
| cost–benefit analysis | 8.7 | 0 | 1 |
| Bukidnon State University | 8.7 | 0 | 1 |
| scikit-learn | 8.7 | 0 | 1 |
| semi-supervised learning | 8.7 | 0 | 1 |
| Strasbourg Astronomical Data Center | 8.7 | 0 | 1 |
| Clonezilla | 8.7 | 0 | 1 |
| Comcast | 8.7 | 0 | 1 |
| Bullet | 8.7 | 0 | 1 |
| conditional random field | 8.7 | 0 | 1 |
| self-organizing map | 8.7 | 0 | 1 |
| IT disaster recovery | 8.7 | 0 | 1 |
| unsupervised learning | 8.7 | 0 | 1 |
| OpenNebula | 8.7 | 0 | 1 |
| National Snow and Ice Data Center | 8.7 | 0 | 1 |
| linear discriminant analysis | 8.7 | 0 | 1 |
| National Earthquake Information Center | 8.7 | 0 | 1 |
| earthDATAsafe | 8.7 | 0 | 1 |
| NASA Space Science Data Coordinated Archive | 8.7 | 0 | 1 |
| Q1407008 | 8.7 | 0 | 1 |
| version space | 8.7 | 0 | 1 |
| capacity planning | 8.7 | 0 | 1 |
| Load balancing | 8.7 | 0 | 1 |
| High-throughput | 8.7 | 0 | 1 |
| Linux-HA | 8.7 | 0 | 1 |
| high availability | 8.7 | 0 | 1 |
| power distribution unit | 8.7 | 0 | 1 |
| IEEE 1355 | 8.7 | 0 | 1 |
| Journal of Machine Learning Research | 8.7 | 0 | 1 |
| spike strip | 8.7 | 0 | 1 |
| adjustment | 8.7 | 0 | 1 |
| switching station | 8.7 | 0 | 1 |
| load balancing | 8.7 | 0 | 1 |
| Q1806786 | 8.7 | 0 | 1 |
| Linux Virtual Server | 8.7 | 0 | 1 |
| Conference on Neural Information Processing Systems | 8.7 | 0 | 1 |
| Pacemaker | 8.7 | 0 | 1 |
| Plesk | 8.7 | 0 | 1 |
| Leibniz Universität IT Services | 8.7 | 0 | 1 |
| Shogun | 8.7 | 0 | 1 |
| Sun Modular Datacenter | 8.7 | 0 | 1 |
| seedbox | 8.7 | 0 | 1 |
| computational learning theory | 8.7 | 0 | 1 |
| Viola–Jones object detection framework | 8.7 | 0 | 1 |
| artificial immune system | 8.7 | 0 | 1 |
| ViaSat-1 | 8.7 | 0 | 1 |
| power usage effectiveness | 8.7 | 0 | 1 |
| GPU cluster | 8.7 | 0 | 1 |
| Linear classifier | 8.7 | 0 | 1 |
| high-availability cluster | 8.7 | 0 | 1 |
| Decision Intelligence | 8.7 | 0 | 1 |
| Junction tree algorithm | 8.7 | 0 | 1 |
| BackupAssist | 8.7 | 0 | 1 |
| Andrei Broder | 8.7 | 0 | 1 |
| Colosse de Québec | 8.7 | 0 | 1 |
| Combinatorial Chemistry & High Throughput Screening | 8.7 | 0 | 1 |
| David M. Blei | 8.7 | 0 | 1 |
| European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | 8.7 | 0 | 1 |
| Gateway Load Balancing Protocol | 8.7 | 0 | 1 |
| cable management | 8.7 | 0 | 1 |
| MARC | 8.7 | 0 | 1 |
| Michael I. Jordan | 8.7 | 0 | 1 |
| Mondo Rescue | 8.7 | 0 | 1 |
| Orange | 8.7 | 0 | 1 |
| Pierre Baldi | 8.7 | 0 | 1 |
| Robert Schapire | 8.7 | 0 | 1 |
| Tanagra | 8.7 | 0 | 1 |
| Usama Fayyad | 8.7 | 0 | 1 |
| World Ocean Atlas | 8.7 | 0 | 1 |
| grid search | 8.7 | 0 | 1 |
| Stuart J. Russell | 8.7 | 0 | 1 |
| FastICA | 8.7 | 0 | 1 |
| genetic improvement | 8.7 | 0 | 1 |
| Pionen | 8.7 | 0 | 1 |
| training, validation, and test data sets | 8.7 | 0 | 1 |
| training set | 8.7 | 0 | 1 |
| High-availability Seamless Redundancy | 8.7 | 0 | 1 |
| World Data Center | 8.7 | 0 | 1 |
| JSM-method | 8.7 | 0 | 1 |
| Investing online | 8.7 | 0 | 1 |
| learning to rank | 8.7 | 0 | 1 |
| ALOPEX | 8.7 | 0 | 1 |
| active learning | 8.7 | 0 | 1 |
| algorithmic learning theory | 8.7 | 0 | 1 |
| Alternating decision tree | 8.7 | 0 | 1 |
| Andrew McCallum | 8.7 | 0 | 1 |
| Apache Mahout | 8.7 | 0 | 1 |
| Bernhard Schölkopf | 8.7 | 0 | 1 |
| BigQuery | 8.7 | 0 | 1 |
| CIML community portal | 8.7 | 0 | 1 |
| Earth Resources Observation and Science (EROS) Center | 8.7 | 0 | 1 |
| Cisco Nexus switches | 8.7 | 0 | 1 |
| Class membership probabilities | 8.7 | 0 | 1 |
| Cloud communications | 8.7 | 0 | 1 |
| Cluster assumption | 8.7 | 0 | 1 |
| Conceptual clustering | 8.7 | 0 | 1 |
| Constantinos Daskalakis | 8.7 | 0 | 1 |
| Constructing skill trees | 8.7 | 0 | 1 |
| CoreSite | 8.7 | 0 | 1 |
| Coupled pattern learner | 8.7 | 0 | 1 |
| Daniel S. Jurafsky | 8.7 | 0 | 1 |
| DataPoint, Inc | 8.7 | 0 | 1 |
| data center environmental control | 8.7 | 0 | 1 |
| data center infrastructure management | 8.7 | 0 | 1 |
| data center infrastructure efficiency | 8.7 | 0 | 1 |
| Data center predictive modeling | 8.7 | 0 | 1 |
| Data center services | 8.7 | 0 | 1 |
| Defense Manpower Data Center | 8.7 | 0 | 1 |
| Google Data Centers | 8.7 | 0 | 1 |
| Digital Realty Trust | 8.7 | 0 | 1 |
| Disaster Recovery Personal Protection Act | 8.7 | 0 | 1 |
| Disaster draft | 8.7 | 0 | 1 |
| Disaster recovery and business continuity auditing | 8.7 | 0 | 1 |
| disaster recovery plan | 8.7 | 0 | 1 |
| early stopping | 8.7 | 0 | 1 |
| Ed H. Chi | 8.7 | 0 | 1 |
| generalization error | 8.7 | 0 | 1 |
| empirical risk minimization | 8.7 | 0 | 1 |
| Encog | 8.7 | 0 | 1 |
| Ensemble averaging | 8.7 | 0 | 1 |
| Eric Horvitz | 8.7 | 0 | 1 |
| Error-driven learning | 8.7 | 0 | 1 |
| MALLET | 8.7 | 0 | 1 |
| factorial code | 8.7 | 0 | 1 |
| Farinaz Koushanfar | 8.7 | 0 | 1 |
| High throughput biology | 8.7 | 0 | 1 |
| GReddy | 8.7 | 0 | 1 |
| geometric feature learning | 8.7 | 0 | 1 |
| Google Modular Data Center | 8.7 | 0 | 1 |
| Green Power Usage Effectiveness | 8.7 | 0 | 1 |
| Grace Wahba | 8.7 | 0 | 1 |
| gradient boosting | 8.7 | 0 | 1 |
| grammar induction | 8.7 | 0 | 1 |
| HAPM | 8.7 | 0 | 1 |
| HP Flexible Data Center | 8.7 | 0 | 1 |
| HP Performance Optimized Datacenter | 8.7 | 0 | 1 |
| HP Utility Data Center | 8.7 | 0 | 1 |
| Candidate-Elimination Algorithm | 8.7 | 0 | 1 |
| High Availability Application Architecture | 8.7 | 0 | 1 |
| High Throughput File System | 8.7 | 0 | 1 |
| hinge loss | 8.7 | 0 | 1 |
| Holger H. Hoos | 8.7 | 0 | 1 |
| IBM 1360 | 8.7 | 0 | 1 |
| transfer learning | 8.7 | 0 | 1 |
| Inferential theory of learning | 8.7 | 0 | 1 |
| eventual consistency | 8.7 | 0 | 1 |
| Jim McIngvale | 8.7 | 0 | 1 |
| Joint Task Force Katrina | 8.7 | 0 | 1 |
| Ken Forbus | 8.7 | 0 | 1 |
| Kevin Leyton-Brown | 8.7 | 0 | 1 |
| large margin nearest neighbor | 8.7 | 0 | 1 |
| learnable evolution model | 8.7 | 0 | 1 |
| Learning automata | 8.7 | 0 | 1 |
| learning with errors | 8.7 | 0 | 1 |
| Leslie P. Kaelbling | 8.7 | 0 | 1 |
| Lianne Dalziel | 8.7 | 0 | 1 |
| outline of machine learning | 8.7 | 0 | 1 |
| Logicalis | 8.7 | 0 | 1 |
| Low Latency Queuing | 8.7 | 0 | 1 |
| low latency | 8.7 | 0 | 1 |
| Low latency | 8.7 | 0 | 1 |
| Machine Learning | 8.7 | 0 | 1 |
| Margin Infused Relaxed Algorithm | 8.7 | 0 | 1 |
| margin | 8.7 | 0 | 1 |
| Margin classifier | 8.7 | 0 | 1 |
| Q6762209 | 8.7 | 0 | 1 |
| Maryland Department of Emergency Management | 8.7 | 0 | 1 |
| meta-learning | 8.7 | 0 | 1 |
| multi-task learning | 8.7 | 0 | 1 |
| National Disaster Recovery Fund | 8.7 | 0 | 1 |
| National Geophysical Data Center | 8.7 | 0 | 1 |
| National disaster recovery framework | 8.7 | 0 | 1 |
| Network Load Balancing | 8.7 | 0 | 1 |
| Never-Ending Language Learning | 8.7 | 0 | 1 |
| Category:Machine learning | 8.7 | 0 | 1 |
| Oak Ridge National Laboratory Distributed Active Archive Center | 8.7 | 0 | 1 |
| Ofer Dekel | 8.7 | 0 | 1 |
| offline machine learning | 8.7 | 0 | 1 |
| online machine learning | 8.7 | 0 | 1 |
| Apache OpenNLP | 8.7 | 0 | 1 |
| Open Data Center Alliance | 8.7 | 0 | 1 |
| PIX | 8.7 | 0 | 1 |
| parity learning | 8.7 | 0 | 1 |
| physical information security | 8.7 | 0 | 1 |
| Piotr Indyk | 8.7 | 0 | 1 |
| Polynomial kernel | 8.7 | 0 | 1 |
| Portable Modular Data Center | 8.7 | 0 | 1 |
| predictive learning | 8.7 | 0 | 1 |
| predictive modelling | 8.7 | 0 | 1 |
| preference learning | 8.7 | 0 | 1 |
| Product of experts | 8.7 | 0 | 1 |
| Pulitzer Center on Crisis Reporting | 8.7 | 0 | 1 |
| Quadratic classifier | 8.7 | 0 | 1 |
| radial basis function kernel | 8.7 | 0 | 1 |
| Random subspace method | 8.7 | 0 | 1 |
| Redo Backup and Recovery | 8.7 | 0 | 1 |
| relevance vector machine | 8.7 | 0 | 1 |
| Richard S. Sutton | 8.7 | 0 | 1 |
| robot learning | 8.7 | 0 | 1 |
| Rule induction | 8.7 | 0 | 1 |
| SARSA | 8.7 | 0 | 1 |
| SeaDataNet | 8.7 | 0 | 1 |
| Semantic analysis | 8.7 | 0 | 1 |
| Seven tiers of disaster recovery | 8.7 | 0 | 1 |
| statistical learning theory | 8.7 | 0 | 1 |
| statistical relational learning | 8.7 | 0 | 1 |
| Structural risk minimization | 8.7 | 0 | 1 |
| Structured SVM | 8.7 | 0 | 1 |
| structured prediction | 8.7 | 0 | 1 |
| Switch and Data | 8.7 | 0 | 1 |
| TIA-942 | 8.7 | 0 | 1 |
| The Green Grid | 8.7 | 0 | 1 |
| Tom M. Mitchell | 8.7 | 0 | 1 |
| Topsham Air Force Station | 8.7 | 0 | 1 |
| Transduction | 8.7 | 0 | 1 |
| Transposition-driven scheduling | 8.7 | 0 | 1 |
| U.S. Bomb Data Center | 8.7 | 0 | 1 |
| Ultra-low latency direct market access | 8.7 | 0 | 1 |
| University of Pittsburgh Epidemiology Data Center | 8.7 | 0 | 1 |
| variational Bayesian methods | 8.7 | 0 | 1 |
| Virtual facility | 8.7 | 0 | 1 |
| container runtime | 8.7 | 0 | 1 |
| Vowpal Wabbit | 8.7 | 0 | 1 |
| Waffles | 8.7 | 0 | 1 |
| weighted majority algorithm | 8.7 | 0 | 1 |
| Westchester Interfaith/Interagency Network for Disaster and Emergency Recovery | 8.7 | 0 | 1 |
| Winnow | 8.7 | 0 | 1 |
| Category:Applied machine learning | 8.7 | 0 | 1 |
| Category:Data mining and machine learning software | 8.7 | 0 | 1 |
| Category:High throughput satellites | 8.7 | 0 | 1 |
| Category:Kernel methods for machine learning | 8.7 | 0 | 1 |
| Category:Machine learning algorithms | 8.7 | 0 | 1 |
| Category:Machine learning researchers | 8.7 | 0 | 1 |
| Covilhã Data Center | 8.7 | 0 | 1 |
| data centre tier | 8.7 | 0 | 1 |
| IBM High Availability Cluster Multiprocessing | 8.7 | 0 | 1 |
| linear regression | 8.7 | 0 | 1 |
| Telegraph Building | 8.7 | 0 | 1 |
| Zhi-Hua Zhou | 8.7 | 0 | 1 |
| Digital Beijing Building | 8.7 | 0 | 1 |
| Sammon projection | 8.7 | 0 | 1 |
| Data center bridging | 8.7 | 0 | 1 |
| Japan Geographic Data Center | 8.7 | 0 | 1 |
| Global Oceanographic DAta Center | 8.7 | 0 | 1 |
| Transportation Bureau Exhibition Center | 8.7 | 0 | 1 |
| disaster recovery | 8.7 | 0 | 1 |
| Q12020374 | 8.7 | 0 | 1 |
| Seoul Data Center | 8.7 | 0 | 1 |
| Utah Data Center | 8.7 | 0 | 1 |
| Category:National Oceanographic Data Center | 8.7 | 0 | 1 |
| network-neutral data center | 8.7 | 0 | 1 |
| high throughput satellite | 8.7 | 0 | 1 |
| Cost efficiency | 8.7 | 0 | 1 |
| John E. Laird | 8.7 | 0 | 1 |
| Field Deployable Hydrolysis System | 8.7 | 0 | 1 |
| International Journal of High Throughput Screening | 8.7 | 0 | 1 |
| Transactions on Machine Learning and Data Mining | 8.7 | 0 | 1 |
| International Journal of Machine Learning and Cybernetics | 8.7 | 0 | 1 |
| Moses Charikar | 8.7 | 0 | 1 |
| Olvi L. Mangasarian | 8.7 | 0 | 1 |
| Lise Getoor | 8.7 | 0 | 1 |
| Alaska Satellite Facility | 8.7 | 0 | 1 |
| Network Load Balancing Services | 8.7 | 0 | 1 |
| Juha Karhunen | 8.7 | 0 | 1 |
| High Throughput X-ray Spectroscopy mission | 8.7 | 0 | 1 |
| Category:Datasets in machine learning | 8.7 | 0 | 1 |
| Category:Modular datacenter | 8.7 | 0 | 1 |
| National Nuclear Data Center | 8.7 | 0 | 1 |
| Lunavi | 8.7 | 0 | 1 |
| Storm Data | 8.7 | 0 | 1 |
| Tree kernel | 8.7 | 0 | 1 |
| Cloud load balancing | 8.7 | 0 | 1 |
| Harvard-MIT Data Center | 8.7 | 0 | 1 |
| Co-training | 8.7 | 0 | 1 |
| feature learning | 8.7 | 0 | 1 |
| Diffbot | 8.7 | 0 | 1 |
| Manifold alignment | 8.7 | 0 | 1 |
| mlpack | 8.7 | 0 | 1 |
| MANIC | 8.7 | 0 | 1 |
| Torch | 8.7 | 0 | 1 |
| International Conference on Machine Learning | 8.7 | 0 | 1 |
| Nearest centroid classifier | 8.7 | 0 | 1 |
| Günter Klambauer | 8.7 | 0 | 1 |
| recovery as a service | 8.7 | 0 | 1 |
| similarity learning | 8.7 | 0 | 1 |
| Platt scaling | 8.7 | 0 | 1 |
| Data center network architectures | 8.7 | 0 | 1 |
| Software-defined data center | 8.7 | 0 | 1 |
| pattern language | 8.7 | 0 | 1 |
| David Tune | 8.7 | 0 | 1 |
| internet data center | 8.7 | 0 | 1 |
| Summa paper mill | 8.7 | 0 | 1 |
| Template:Machine learning bar | 8.7 | 0 | 1 |
| engine power plant | 8.7 | 0 | 1 |
| Cloud mining | 8.7 | 0 | 1 |