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A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

From the Publisher: This book brings together - in an informal and tutorial fashion - the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic al...

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing mach...

Subjects / keywords: Computer science; Overfitting; Ensemble learning; Boosting (machine learning); AdaBoost; Artificial intelligence; Machine learning; Classifier (UML); Bayesian probability; Pattern recognition (psychology); Artificial neu...

Subjects / keywords: Extreme learning machine; Computer science; Bottleneck; Generalization; Feedforward neural network; Artificial neural network; Benchmark (surveying); Feed forward; Artificial intelligence; Key (lock); Machine learning; A...

Subjects / keywords: Computer science; Artificial intelligence; Machine learning; Multiphysics; Inference; Artificial neural network; Physical law; Field (mathematics); Discretization; Kernel method; Deep learning; Theoretical computer scien...

Subjects / keywords: Support vector machine; Computer science; Machine learning; Artificial intelligence; Categorization; Text categorization; Task (project management); Variety (cybernetics); Empirical research; Relevance vector machine; Ma...

Subjects / keywords: Algorithm; Receiver operating characteristic; Machine learning; Artificial intelligence; Perceptron; Computer science; Discriminant; Multilayer perceptron; Mathematics; Artificial neural network

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of t...

Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same do...

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors ...

In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in ...

Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance ...

A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this...

Statistical learning theory was introduced in the late 1960's. Until the 1990's it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990's new ...

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a ...

We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged enco...

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad...

This 1996 book is a reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theor...

An attentional mechanism has lately been used to improve neural machine translation (NMT) by selectively focusing on parts of the source sentence during translation. However, there has been little work exploring useful a...

My first exposure to Support Vector Machines came this spring when heard Sue Dumais present impressive results on text categorization using this analysis technique. This issue's collection of essays should help familiari...

Subjects / keywords: Boosting (machine learning); Multiplicative function; Bounded function; Computer science; Generalization; Mathematical optimization; Decision rule; Artificial intelligence; Mathematics; Machine learning; Algorithm; Mathe...

Subjects / keywords: Artificial intelligence; Deep learning; Computer science; Artificial neural network; Backpropagation; Reinforcement learning; Machine learning; Deep neural networks; Unsupervised learning; Recurrent neural network; Encod...

Subjects / keywords: Reinforcement learning; Computer science; Artificial intelligence; Variety (cybernetics); Deep learning; Control (management); Perception; Human–computer interaction; Machine learning; Neuroscience; Biology