This paper proposes a novel computational intelligence technique, based on the sociological concept of human group formation, with the aim to acquire a better solution to classification problems. The key concept of the human group formation is about the behavior of in-group members that try to unite with their own group as much as possible, and at the same time maintain social distance from the...

We address the problem of fast figure-ground segmentation of single objects from cluttered backgrounds to improve object learning and recognition. For the segmentation, we use an initial foreground hypothesis to train a classifier for figure and ground on topographically ordered feature maps with Generalized Learning Vector Quantization. We investigate the contribution of several adaptive metri...

We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. Training is based on the current annotation of the genome. Relevance learning techniques are used to weight and select features according to their importance for the classification. Among other findings, the analysis su...

Most existing systems of speaker recognition use “state of the art” acoustic features. However, many times one can only recognize a speaker by his or her prosodic features, especially by the accent. For this reason, the authors investigate some pertinent prosodic features that can be associated with other classic acoustic features, in order to improve the recognition accuracy. The authors have ...

We present a technique to extend Robust Soft Learning Vector Quantization (RSLVQ). This algorithm is derived from an explicit cost function and follows the dynamics of a stochastic gradient ascent. The RSLVQ cost function involves a hyperparameter which is kept fixed during training. We propose to adapt the hyperparameter based on the gradient information. Experiments on artificial and real lif...

This paper presents a cursive character recognizer, a crucial module in any Cursive Script Recognition system based on a segmentation and recognition approach. The character classi2cation is achieved by combining the use of neural gas (NG) and learning vector quantization (LVQ). NG is used to verify whether lower and upper case version of a certain letter can be joined in a single class or not....

Document categorization is a daily task in every organization, but it is a very subjective process. While automatic document categorization has been widely studied, much challenging research still remains to support user subjective categorization. This study evaluates and compares the application of Self-Organizing Maps (SOM) and Learning Vector Quantization (LVQ) to automatic document classifi...

Classifying large datasets without any a-priori information poses a problem especially in the field of bioinformatics. In this work, we explore the problem of classifying hundreds of thousands of cell assay images obtained by a highthroughput screening camera. The goal is to label a few selected examples by hand and to automatically label the rest of the images afterwards. We deal with three ma...

Reject option is a technique used to improve classifier’s reliability in decision support systems. It consists on withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue have been concerned with implementing a reject...

We derive a novel derivative based version of kernelized Generalized Learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on UCI data and additionally show that automati...