Reinforcement learning has proven to be a set of successful techniques for finding optimal policies on uncertain and/or dynamic domains, such as the RoboCup. One of the problems on using such techniques appears with large state and action spaces, as it is the case of input information coming from the Robosoccer simulator. In this paper, we describe a new mechanism for solving the states general...

Machine learning algorithms have been shown to be highly effective in solving optimization problems a wide range of applications. Such typically use gradient descent with backpropagation and the chain rule. Hence, fails if intermediate gradients are zero for some functions computational graph, because it causes collapse when multiplying zero. Vector quantization is one those challenging machine...

A large variety of machine learning models which aim at vector quantization have been proposed. However, only very preliminary rigorous mathematical analysis concerning their learning behavior such as convergence speed, robustness with respect to initialization, etc. exists. In this paper, we use the theory of on-line learning for an exact mathematical description of the training dynamics of Ve...

This paper presents a study and implementation of still image compression using learned vector quantization. Grey scale, still images are compressed by 16:1 and transmitted at 0.5 bits per pixel, while maintaining a peak signal-to-noise ratio of 30 dB. The vector quantization is learned using Kohonen’s self organizing feature map (SOFM). While not only being representative of the training set, ...

In the emerging area of wireless sensor networks, one of the most typical challenges is to retrieve historical information from the sensor nodes. Due to the resource limitation of sensor nodes (processing, memory, bandwidth, and energy), the collected information of sensor nodes has to be compressed quickly and precisely for transmission. In this paper, we propose a new technique -the ALVQ (Ado...

An application of the recently proposed generalized relevance learning vector quantization (GRLVQ) to the analysis and modeling of time series data is presented. We use GRLVQ for two tasks: first, for obtaining a phase space embedding of a scalar time series, and second, for short term and long term data prediction. The proposed embedding method is tested with a signal from the wellknown Lorenz...

Generalized learning vector quantization (GRLVQ) is a prototype based classification algorithm with metric adaptation weighting each data dimensions according to their relevance for the classification task. We present in this paper an extension for functional data, which are usually very high dimensional. This approach supposes the data vectors have to be functional representations. Taking into...