The regularized LVQ1 algorithm

An Improved Lvq Algorithm with Data-structure Preserving Visualization

Data-structure preserved visualization of high-dimensional data reveals the dataset borders and the spread and overlapping tendency of the class borders in a more informative manner than the usual data-topology preserved mapping produced by SelfOrganizing Maps (SOMs). Hence, an extension of SOM called Probabilistic Regularized SOM (PRSOM) is proposed for the data-structure preservation in the v...

Neural Techniques for Improving the Classification Accuracy of Microarray Data Set using Rough Set Feature Selection Method

-Classification, a data mining task is an effective method to classify the data in the process of Knowledge Data Discovery. Classification method algorithms are widely used in medical field to classify the medical data for diagnosis. Feature Selection increases the accuracy of the Classifier because it eliminates irrelevant attributes. This paper analyzes the performance of neural network class...

Performance analysis of LVQ algorithms: A statistical physics approach

Learning vector quantization (LVQ) constitutes a powerful and intuitive method for adaptive nearest prototype classification. However, original LVQ has been introduced based on heuristics and numerous modifications exist to achieve better convergence and stability. Recently, a mathematical foundation by means of a cost function has been proposed which, as a limiting case, yields a learning rule...

Regularized Autoregressive Multiple Frequency Estimation

The paper addresses a problem of tracking multiple number of frequencies using Regularized Autoregressive (RAR) approximation. The RAR procedure allows to decrease approximation bias, comparing to other AR-based frequency detection methods, while still providing competitive variance of sample estimates. We show that the RAR estimates of multiple periodicities are consistent in probabilit...

Optimal decision surfaces in LVQ1 classiffication of patterns