Методы и алгоритмы коллективного распознавания: обзор

Collective recognition is a task in which multiple classifiers are used for making decisions concerning the class of the same entity (object, situation, etc.) with the subsequent combining and agreement of individual decisions based on some special algorithm. Currently this research direction in the pattern recognition and classification scope is of topmost interest within information technology community. The reason of this fact is that methods and techniques of collective recognition demonstrate new capabilities in regard to pattern recognition and classification accuracy, on the one hand, and they are increasingly used in complex large scale applications. This paper surveys state of the art of the research on the scope in question covering the period from 1950-th and till now. — Bibl. 70 items.Под коллективным распознаванием понимается задача использования множества классификаторов, каждый из которых принимает решение о классе одной и той же сущности, ситуации, образа и т.п., с последующим объединением и согласованием решений отдельных классификаторов с помощью того или иного алгоритма. В настоящее время это направление в области распознавания образов и классификации, которое, с одной стороны, зарекомендовало себя как новый шаг в данной области, и которое, с другой стороны, находит все более и более широкое применение в решении сложных крупномасштабных прикладных задач, является предметом активных теоретических и прикладных исследований. Данная работа посвящена обзору состояния исследований в упомянутой области начиная с первых работ, которые относятся еще к 1950-м годам, и заканчивая самыми новыми результатами

Mixtures of Experts Estimate A Posteriori Probabilities

The mixtures of experts (ME) model offers a modular structure suitable for a divide-and-conquer approach to pattern recognition. It has a probabilistic interpretation in terms of a mixture model, which forms the basis for the error function associated with MEs. In this paper, it is shown that for classification problems the minimization of this ME error function leads to ME outputs estimating the a posteriori probabilities of class membership of the input vector

Mixtures of experts estimate a posteriori probabilities

The mixtures of experts (ME) model offers a modular structure suitable for a divide-and-conquer approach to pattern recognition. It has a probabilistic interpretation in terms of a mixture model, which forms the basis for the error function associated with MEs. In this paper, it is shown that for classification problems the minimization of this ME error function leads to ME outputs estimating the a posteriori probabilities of class membership of the input vector

Mixtures of Experts Estimate A Posteriori Probabilities

The mixtures of experts (ME) model offers a modular structure suitable for a divide-and-conquer approach to pattern recognition. It has a probabilistic interpretation in terms of a mixture model, which forms the basis for the error function associated with MEs. In this paper, it is shown that for classification problems the minimization of this ME error function leads to ME outputs estimating the a posteriori probabilities of class membership of the input vector. 1 Introduction It is well-known that for artificial neural networks trained by minimizing sum-ofsquares or cross-entropy error functions for a classification problem, the network outputs approximate the a posteriori probabilities of class membership [2]. This property is a very useful one, especially when the network outputs are to be used in a further decision-making stage (e.g. rejection thresholds) or integrated in other statistical pattern recognition methods (as in hybrid NN-HMMs). Recently, a modular architecture of neu..

Committee machines: a unified approach using support vector machines

Orientador : Fernando Jose Von ZubenTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoResumo: Os algoritmos baseados em métodos de kernel destacam-se entre as diversas técnicas de aprendizado de máquina. Eles foram inicialmente empregados na implementação de máquinas de vetores-suporte (SVMs). A abordagem SVM representa um procedimento de aprendizado não-paramétrico para classificação e regressão de alto desempenho. No entanto, existem aspectos estruturais e paramétricos de projeto que podem conduzir a uma degradação de desempenho. Na ausência de uma metodologia sistemática e de baixo custo para a proposição de modelos computacionais otimamente especificados, os comitês de máquinas se apresentam como alternativas promissoras. Existem versões estáticas de comitês, na forma de ensembles de componentes, e versões dinâmicas, na forma de misturas de especialistas. Neste estudo, os componentes de um ensemble e os especialistas de uma mistura são tomados como SVMs. O objetivo é explorar conjuntamente potencialidades advindas de SVM e comitê de máquinas, adotando uma formulação unificada. Várias extensões e novas configurações de comitês de máquinas são propostas, com análises comparativas que indicam ganho significativo de desempenho frente a outras propostas de aprendizado de máquina comumente adotadas para classificação e regressãoAbstract: Algorithms based on kernel methods are prominent techniques among the available approaches for machine learning. They were initially applied to implement support vector machines (SVMs). The SVM approach represents a nonparametric learning procedure devoted to high performance classification and regression tasks. However, structural and parametric aspects of the design may guide to performance degradation. In the absence of a systematic and low-cost methodology for the proposition of optimally specified computational models, committee machines emerge as promising alternatives. There exist static versions of committees, in the form of ensembles of components, and dynamic versions, in the form of mixtures of experts. In the present investigation, the components of an ensemble and the experts of a mixture are taken as SVMs. The aim is to jointly explore the potentialities of both SVM and committee machine, by means of a unified formulation. Several extensions and new configurations of committee machines are proposed, with comparative analyses that indicate significant gain in performance before other proposals for machine learning commonly adopted for classification and regressionDoutoradoEngenharia de ComputaçãoDoutor em Engenharia Elétric