نتایج جستجو برای: pattern selection
تعداد نتایج: 652957 فیلتر نتایج به سال:
This paper presents an overview of Feature Extraction techniques for off-line recognition of isolated Gurumukhi numerals/characters. Selection of Feature Extraction method is probably the single most important factor in achieving high performance in pattern recognition. Our paper presents Zone based hybrid approach which is the combination of image centroid zone and zone centroid zone of numera...
A new face recognition system is introduced and analyzed in this paper. The system utilizes a novel statistical pattern recognition method to optimize the feature selection process. The optimized feature set feeds a classification module, which is based on the modified ν-support vector machine approach (ν-SVM). The optimized feature set reduces the burden of the subsequent ν-SVM classifier and ...
This paper proposes an argument that explains incumbency advantage without recurring to the collective irresponsibility of legislatures. For that purpose, we exploit the informational value of incumbency: incumbency confers voters information about governing politicians not available from challengers. Because there are many reasons for high reelection rates di¤erent from incumbency status, we p...
The paper gives an overview of feature selection techniques in statistical pattern recognition with particular emphasis on methods developed within the Institute of Information Theory and Automation research team throughout recent years. Besides discussing the advances in methodology since times of Perez’s pioneering work the paper attempts to put the methods into a taxonomical framework. The m...
Given a large set of potential features, it is usually necessary to find a small subset with which to classify. The task of finding an optimal feature set is inherently combinatoric and therefore suboptimal algorithms are typically used to find feature sets. If feature selection is based directly on classification error, then a feature-selection algorithm must base its decision on error estimat...
The combination of classi"ers leads to substantial reduction of misclassi"cation error in a wide range of applications and benchmark problems. We suggest using an out-of-bag sample for combining di0erent classi"ers. In our setup, a linear discriminant analysis is performed using the observations in the out-of-bag sample, and the corresponding discriminant variables computed for the observations...
Personal service robots assisting humans in daily life are expected to provide a user-oriented service [1]. To meet this expectation, there has been much research on behavior selection algorithms for the proper service. The action selection method was proposed to activate a most appropriate action in a certain situation [2]. The behavior selection method for artificial creatures considering bot...
Feature selection is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. In this work, we propose a new concept of neighborhood margin and neighborhood soft margin to measure the minimal distance between different classes. We use the criterion of neighborhood soft ma...
Classification is a method of accurately predicting the target class for an unlabelled sample by learning from instances described by a set of attributes and a class label. Instance based classifiers are attractive due to their simplicity and performance. However, many of these are susceptible to noise and become unsuitable for real world problems. This paper proposes a novel instance based cla...
We deal with two important problems in pattern recognition that arise in the analysis of large datasets. While most feature subset selection methods use statistical techniques to preprocess the labeled datasets, these methods are generally not linked with the combinatorial properties of the final solutions. We prove that it is NP−hard to obtain an appropriate set of thresholds that will transfo...
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