These datasets are used in machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field...
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paradigm Force control List of important publications in machine learning List of datasets for machine-learning research M-theory (learning framework) The definition...
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classifiers Cross-validation List of datasets for machine learning research scikit-learn, an open source machine learning library for Python Orange, a free data...
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mix of the smallest and largest Ws. List of datasets for machine learning research Sample complexity Bayesian Optimization Reinforcement learning Improving...
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In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating...
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theory List of artificial intelligence projects List of datasets for machine learning research History of machine learning Timeline of machine learning Machine...
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includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial...
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In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which...
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list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of...
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Training, validation, and test data sets (redirect from Dataset (machine learning))
List of datasets for machine learning research Hierarchical classification Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning....
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spaces List of datasets for machine learning research Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) Foundations of Machine Learning, The MIT...
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In machine learning, a learning curve (or training curve) plots the optimal value of a model's loss function for a training set against this loss function...
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Attention is a machine learning method that determines the relative importance of each component in a sequence relative to the other components in that...
44 KB (4,841 words) - 04:11, 31 August 2024
Multimodal learning, in the context of machine learning, is a type of deep learning using multiple modalities of data, such as text, audio, or images...
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Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...
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Multilayer perceptron (section Learning)
language model". The Journal of Machine Learning Research. 3: 1137–1155. "Papers with Code – MLP-Mixer: An all-MLP Architecture for Vision". Haykin, Simon (1998)...
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Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves...
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conferences of high impact in machine learning and artificial intelligence research. It is supported by the International Machine Learning Society (IMLS)...
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Feature engineering (redirect from Feature extraction (machine learning))
extraction Feature learning Hashing trick Instrumental variables estimation Kernel method List of datasets for machine learning research Scale co-occurrence...
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Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...
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In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration...
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Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory...
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Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching...
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Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...
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A transformer is a deep learning architecture developed by researchers at Google and based on the multi-head attention mechanism, proposed in a 2017 paper...
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machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future...
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reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem of a computational...
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In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into...
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Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled...
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In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an agent with human preferences. It involves training a...
43 KB (4,925 words) - 23:00, 24 August 2024