Classification is an active topic of Machine Learning. The most recent achievements in this domain suggest using ensembles of learners instead of a single classifier to improve classification accuracy. Comparisons between Bagging and Boosting show that classifier ensembles perform better when their members exhibit diversity, that is commit different errors. This paper proposes a genetic algorit...

We introduce a probabilistic model that combines a classifier with an extra reinforcement signal (RS) encoding the probability of an erroneous feedback being delivered by the classifier. This representation computes the class probabilities given the task related features and the reinforcement signal. Using expectation maximization (EM) to estimate the parameter values under such a model shows t...

In this paper a hybrid adaptive particle swarm optimization aided learnable Bayesian classifier is proposed. The objective of the classifier is to solve some of the fundamental problems associated with the pure naive Bayesian classifier and its variants with a larger view towards maximization of the classifier accuracy. Further, the proposed algorithm can exhibits an improved capability to elim...

In named entity recognition (NER) for biomedical literature, approaches based on combined classifiers have demonstrated great performance improvement compared to a single (best) classifier. This is mainly owed to sufficient level of diversity exhibited among classifiers, which is a selective property of classifier set. Given a large number of classifiers, how to select different classifiers to ...

This paper focuses on the circuit aspects required for an on-chip, on-line SoC large-scale Field Programmable Analog Array (FPAA) learning for Vector-Matrix Multiplier (VMM) + Winner-Take-All (WTA) classifier structure. We start by describing the VMM+WTA classifier structure, and then show techniques required to handle device mismatch. The approach is initially explained using a VMM+WTA as a tw...

We describe a neural network learning algorithm that implements differential learning in a generalized backpropagation framework. The algorithm regulates model complexity during the learning procedure, generating the best low-complexity approximation to the Bayes-optimal classifier allowed by the training sample. It learns to recognize handwritten digits of the AT&T DB1 database. Learning is do...

We propose a hybrid hierarchical classifier that solves multiclass problems in high dimensional space using a set of binary classifiers arranged as a tree in the space of classes. It incorporates good aspects of both the binary hierarchical classifier (BHC) and the margin tree algorithm, and is effective over a large range of (sample size, input dimensionality) values. Two aspects of the propos...