We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate...

Purpose – The purpose of this paper is to perform a comparative study of prediction performances of an artificial neutral network (ANN) model against a linear prediction model like a linear discriminant analysis (LDA) with regards to forecasting corporate credit ratings from financial...

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The huge popularity of Hidden Markov models in pattern recognition is due to the ability to 'learn' model parameters from an observation sequence through Baum-Welch and other re-estimation procedures. In the case of HMM parameter estimation from an ensemble of observation sequences, rather than...

Previous research has suggested that depth of search in chess does not increase much as a function of skill. We submitted players to a problem-solving task with complex positions. We found a strong skill effect in depth of search, rate of search, and number of nodes generated. At the level of...

This thesis is based on research on financial time series analysis using pattern recognition methods. The first part of this research focuses on univariate time series analysis using different pattern recognition methods. First, probabilities of basic patterns are used to represent the features...

Cycle-theory-based market analysis is the main focus of this thesis, in which I try to find systematic methods to recognize and utilize market patterns, especially cyclic ones, to obtain a correct understanding of market movements. The KNN algorithm, a pattern recognition method extensively used...

In this paper, we develop a new framework for face recognition based on nonparametric discriminant analysis (NDA) and multi-classifier integration. Traditional LDA-based methods suffer a fundamental limitation originating from the parametric nature of scatter matrices, which are based on the...

The study of long term stability in particle accelerators has long been served by a group of widely circulated computer programs. The progress in these programs has mirrored the growth and versatility in accelerator size, complexity, and purpose, as well as evolving technologies in computing...

The study of long term stability in particle accelerators has long been served by a group of widely circulated computer programs. The progress in these programs has mirrored the growth and versatility in accelerator size, complexity, and purpose, as well as evolving technologies in computing...