PhD Thesis in Computer Science written by María Teresa Martín Valdivia under the supervision of Dr. L. Alfonso Ureña López (Univ. of Jaén) and Dr. Francisco Triguero Ruiz (Univ. of Málaga). The author was examined in May 6 2004 by the commitee formed by Dr. Manual Palomar Sanz (Univ. of Alicante), Dr. Amparo Ruiz Sepúlveda (Univ. of Málaga), Dr. Emilio Sanchís Arnal (Univ. Politécnica of Valenc...

Winner-Takes-All (WTA) algorithms offer intuitive and powerful learning schemes such as Learning Vector Quantization (LVQ) and variations thereof, most of which are heuristically motivated. In this article we investigate in an exact mathematical way the dynamics of different vector quantization (VQ) schemes including standard LVQ in simple, though relevant settings. We consider the training fro...

This paper shows that the distributed representation found in Learning Vector Quantization (LVQ) enables reinforcement learning methods to cope with a large decision search space, defined in terms of equivalence classes of input patterns like those found in the game of Go. In particular, this paper describes S[arsa]LVQ, a novel reinforcement learning algorithm and shows its feasibility for patt...

Kohonen neural nets are some kind of competitive nets. The most commonly known variants are the Self-Organizing Maps (SOMs) and the Learning Vector Quantization (LVQ). The former model uses an unsupervized learning, the latter is an e cient classi er. This paper tries to give, in simple words, a clear idea about the basis of competitive neural nets and competitive learning emphasizing on the SO...

Prototype-based classification models, and particularly Learning Vector Quantization (LVQ) frameworks with adaptive metrics, are powerful supervised classification techniques with good generalization behaviour. This thesis proposes three advanced learning methodologies, in the context of LVQ, aiming at better classification performance under various classification settings. The first contributi...

In this paper we describe a method of learning hierarchical representations for describing and recognizing gestures expressed as one and two arm movements using competitive learning methods. At the low end of the hierarchy, the atomic motions (“letters”) corresponding to flow fields computed from successive color image frames are derived using Learning Vector Quantization (LVQ). At the next int...

Identifying clusters is an important aspect of data analysis. This paper proposes a noveldata clustering algorithm to increase the clustering accuracy. A novel game theoretic self-organizingmap (NGTSOM ) and neural gas (NG) are used in combination with Competitive Hebbian Learning(CHL) to improve the quality of the map and provide a better vector quantization (VQ) for clusteringdata. Different ...

This paper presents an axiomatic approach to soft learning vector quantization (LVQ) and clustering based on reformulation. The reformulation of the fuzzy c-means (FCM) algorithm provides the basis for reformulating entropy-constrained fuzzy clustering (ECFC) algorithms. This analysis indicates that minimization of admissible reformulation functions using gradient descent leads to a broad varie...

In keystroke dynamics-based authentication, novelty detection methods have been used since only the valid user’s patterns are available when a classifier is built. After a while, however, impostors’ keystroke patterns become also available from failed login attempts. We propose to retrain the novelty detector with the impostor patterns to enhance the performance. In this paper the support vecto...