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...

mix of the smallest and largest Ws. List of datasets for machine learning research Sample complexity Bayesian Optimization Reinforcement learning Improving...

Programming paradigm Force control List of important publications in machine learning List of datasets for machine-learning research The definition "without being...

classifiers Cross-validation List of datasets for machine learning research scikit-learn, an open source machine learning library for Python Orange, a free data...

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating...

list of datasets for machine learning research. It is part of the list of datasets for machine-learning research. These datasets consist primarily of...

spaces List of datasets for machine learning research Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar (2012) Foundations of Machine Learning, The MIT...

The Machine learning-based attention method simulates how human attention works by assigning varying levels of importance to different words in a sentence...

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves...

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...

conferences of high impact in machine learning and artificial intelligence research. It is supported by the International Machine Learning Society (IMLS)...

theory List of artificial intelligence projects List of datasets for machine learning research History of machine learning Timeline of machine learning Machine...

List of datasets for machine learning research Hierarchical classification Ron Kohavi; Foster Provost (1998). "Glossary of terms". Machine Learning....

includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial...

transformation Feature extraction Feature learning Hashing trick Kernel method List of datasets for machine learning research Space mapping Instrumental variables...

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...

variation has been prevalently adopted for training large language models (LLM) on large (language) datasets, such as the Wikipedia corpus and Common...

Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...

Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning...

Mamba is a deep learning architecture focused on sequence modeling. It was developed by researchers from Carnegie Mellon University and Princeton University...

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration...

Multimodal learning, in the context of machine learning, is a type of deep learning using a combination of various modalities of data, such as text, audio...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

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...

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...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent to human preferences. In classical...

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory...

his book Perceptron. This extreme learning machine was not yet a deep learning network. In 1965, the first deep-learning feedforward network, not yet using...

In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into...

learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with...