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...

An electronic nose system had been developed by using 16 quartz resonator sensitive membranesbasic resonance frequencies 20 MHz as a sensor, and analyzed the measurement data through various neural network as a pattern recognition system. The developed system showed high recognition probability to discriminate various single odors even mixture odor to its high generality properties; however the...

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...

The nearest neighbor method together with the dynamic time warping (DTW) distance is one of the most popular approaches in time series classification. This method suffers from high storage and computation requirements for large training sets. As a solution to both drawbacks, this article extends learning vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ scheme uses...

We introduce a novel way to employ codebooks trained by Learning Vector Quantization together with hidden Markov models. In previous work, LVQ-codebooks have been used as frame labelers. The resulting label stream has been modeled and decoded by discrete observation HMMs. We present a way to extract more information out of the LVQ stage. This is accomplished by modeling the class-wise quantizat...

In this contribution, we focus on reject options for prototypebased classifiers, and we present a comparison of reject options based on statistical models for prototype-based classification as compared to alternatives which are motivated by simple geometric principles. We compare the behavior of generative models such as Gaussian mixture models and discriminative ones to results from robust sof...

We present two approaches to extend Robust Soft Learning Vector Quantization (RSLVQ). This algorithm for nearest prototype classification is derived from an explicit cost function and follows the dynamics of a stochastic gradient ascent. The RSLVQ cost function is defined in terms of a likelihood ratio and involves a hyperparameter which is kept constant during training. We propose to adapt the...

Various prototype based learning techniques have recently been extended to similarity data by means of kernelization. While stateof-the-art classification results can be achieved this way, kernelization loses one important property of prototype-based techniques: a representation of the solution in terms of few characteristic prototypes which can directly be inspected by experts. In this contrib...