In machine learning, multiple-instance learning (MIL) is a type of supervised learning. Instead of receiving a set of instances which are individually...

Multi-task learning Multilinear subspace learning Multimodal learning Multiple instance learning Multiple-instance learning Never-Ending Language Learning Offline...

In machine learning, multi-label classification or multi-output classification is a variant of the classification problem where multiple nonexclusive...

Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression...

learning efficiency. Since transfer learning makes use of training with multiple objective functions it is related to cost-sensitive machine learning...

of distance learning. This is the first known instance of the use of materials for independent language study. The concept of e-learning began developing...

test instance to be generated by the model. Robot learning is inspired by a multitude of machine learning methods, starting from supervised learning, reinforcement...

Search MPEG-7 Multimedia information retrieval Multiple-instance learning Nearest neighbor search Learning to rank Content-based Multimedia Information...

parsed into sub-types. For instance, declarative memory comprises both episodic and semantic memory. Non-associative learning refers to "a relatively permanent...

pharmaceuticals. Federated learning aims at training a machine learning algorithm, for instance deep neural networks, on multiple local datasets contained...

Instantaneously trained neural networks Latent semantic indexing Multiple-instance learning Naive Bayes classifier Natural language processing approaches...

machines. An alternative approach uses multiple-instance learning by encoding molecules as sets of data instances, each of which represents a possible molecular...

algorithm to accurately determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

manner. Functional genomics and computational approaches based on multiple instance learning have also been developed to integrate RNA-seq data to predict...

rote learning eschews comprehension, so by itself it is an ineffective tool in mastering any complex subject at an advanced level. For instance, one illustration...

learner has learned is an instance of meaningful learning. Utilization of meaningful learning may trigger further learning, as the relation of a concept...

possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables are termed features...

deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models...

Concept learning, also known as category learning, concept attainment, and concept formation, is defined by Bruner, Goodnow, & Austin (1956) as "the search...

Nested Multi-Instance Learning". arXiv:1602.08033 [cs.SI]. Buza, Krisztian. "Feedback prediction for blogs."Data analysis, machine learning and knowledge...

and more general to have multiple sources of information with differing reliabilities or areas of expertise. Active learning also assumes that the single...

In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations...

Z., Madabhushi, A., Gao, P., Cong, F., & Lu, C. (2025). When multiple instance learning meets foundation models: Advancing histological whole slide image...

class of supervised neural network models). In recent developments of deep learning the rectified linear unit (ReLU) is more frequently used as one of the...

practice of educational approaches to learning. Educational technology as technological tools and media, for instance massive online courses, that assist...

Weak supervision (also known as semi-supervised learning) is a paradigm in machine learning, the relevance and notability of which increased with the...

Learning disability, learning disorder, or learning difficulty (British English) is a condition in the brain that causes difficulties comprehending or...

A learning curve is a graphical representation of the relationship between how proficient people are at a task and the amount of experience they have....

provided, consisting of a set of instances that have been properly labeled by hand with the correct output. A learning procedure then generates a model...