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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 ...

Materias / palabras clave: Sociology; Social theory; Epistemology; Social learning; Social learning theory; Social science; Psychology; Social psychology; Philosophy; Pedagogy

Materias / palabras clave: Generalization; Statistical learning theory; Statistical learning; Learning theory; Function (biology); Statistical theory; Epistemology; Computer science; Artificial intelligence; Mathematics; Mathematics education; Phi...

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Mach...

Ch. Argyris, D. A. Schön, Organizational Learning: A Theory of Action Perspective, Reis: Revista Española de Investigaciones Sociológicas, No. 77/78, Monográfico sobre la Formación y las Organizaciones (Jan. - Jun.,...

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

This is the first comprehensive introduction to Support Vector Machines (SVMs), a generation learning system based on recent advances in statistical learning theory. SVMs deliver state-of-the-art performance in real-worl...

Leon Festinger's theory of cognitive dissonance has been widely recognized for its important and influential concepts in areas of motivation and social psychology. The theory of dissonance is here applied to the problem ...

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be...

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science a...

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...

I. Introduction 1. Self-Determination Theory: An Introduction and Overview II. Philosophical and Historical Considerations 2. Organismic Principles: Historical Perspectives on Development and Integration in Living Entiti...

Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be...

Materias / palabras clave: Bootstrap aggregating; Regression; Artificial intelligence; Machine learning; Set (abstract data type); Linear regression; Class (philosophy); Outcome (game theory); Selection (genetic algorithm); Stability (learning the...

A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of the classification functions, including Perceptrons, p...

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a de...

Given a combinatorial optimisation problem, there are typically multiple ways of modelling it for presentation to an automated solver. Choosing the right combination of model and target solver can have a significant impa...

Tree boosting is a highly effective and widely used machine learning method. In this paper, we describe a scalable end-to-end tree boosting system called XGBoost, which is used widely by data scientists to achieve state-...

The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference. The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference ...

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solvin...

Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn th...

Papers from the 2006 flagship meeting on neural computation, with contributions from physicists, neuroscientists, mathematicians, statisticians, and computer scientists. The annual Neural Information Processing Systems (...

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describe...

This book presents a theory of learning that starts with the assumption that engagement in social practice is the fundamental process by which we get to know what we know and by which we become who we are. The primary un...