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Information granules emerging as a result of an abstract and more condensed and global view at numeric data play an essential role in various pattern recognition pursuits. In this study, we investigate an idea of granular prototypes (representatives) and discuss their role in the realization of classification schemes. A two-stage procedure of a formation of information granules is discussed. We show...
This paper investigates the ability of an evolutionary pruning mechanism to improve the predictive accuracy of a classifier based on non-nested generalized exemplars. Two pruning algorithms are proposed: one which selects the most representative generalized exemplars and the other one which simultaneously selects both relevant exemplars and relevant attributes. Experimental studies conducted for a...
The Self-Organizing Maps have been widely used as multidimensional unsupervised classifiers. The aim of this paper is to develop a self-organizing map for interval data. Due to the increasing use of such data in Data Mining, many clustering methods for interval data have been proposed this last decade. In this paper, we propose an algorithm to train the self-organizing map for interval data. We use...
This paper investigates a method for instance selection in the context of supervised classification adapted to large databases. Based on the scale up concept, the method reduces the time required to perform the selection procedure by enabling the application of known condensation instance techniques to only small data sets instead of the whole set. The novelty of our approach relies in the way of...
In this paper, we extend LELC (PU Learning by Extracting Likely Positive and Negative Micro-Clusters) method to cope with positive and unlabeled data streams. Our developed approach, which is called vote-based LELC, works in three steps. In the first step, we extract representative documents from unlabeled data and assign a vote score to each document. The assigned vote score reflects the degree of...
Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective model for topics representation. On the basis of K-nearest neighbor (KNN) algorithm for text classification and support vector machines (SVM) algorithm for text classification, we have studied how they affect topic tracking. Then we get the variation law that they affect...
This paper presents a text query-based method for keyword spotting from online Chinese handwritten documents. The similarity between a text word and handwriting is obtained by combining the character similiarity scores given by a character classifier. To overcome the ambiguity of character segmentation, multiple candidates of character patterns are generated by over-segmentation, and sequences of...
This paper presents an off-line signature verification system composed of a combination of several different classifiers. Identity authentication is a very important characteristics specially in systems that requires a high degree of security such as in bank transactions. In our experiments, one-class classifier was used to create a signature verification system, consequently only genuine signatures...
A supervised nonlinear classification approach is proposed in this paper. It can classify data in original feature space without concerning kernel transformation to map data into linear high dimension space, Belonging degree measure used in this approach is more rational than some conventional distance measures such as Euclidean distance, Under ERM principle, union of hyper ellipsoids and hyper planes...
Traditional pattern recognition techniques can not handle the classification of large datasets with both efficiency and effectiveness. In this context, the Optimum-Path Forest (OPF) classifier was recently introduced, trying to achieve high recognition rates and low computational cost. Although OPF was much faster than Support Vector Machines for training, it was slightly slower for classification...
Classifier ensembles based on selection-fusion strategy have recently aroused enormous interest. The main idea underlying this strategy is to use miniensembles instead of monolithic base classifiers in an ensemble in order to improve the overall performance. This paper proposes a classifier selection method to be used in selection-fusion strategies. The method involves first splitting the original...
Prototype classifiers trained with multi-class classification objective are inferior in pattern retrieval and outlier rejection. To improve the binary classification (detection, verification, retrieval, outlier rejection) performance of prototype classifiers, we propose a one-vs-all training method, which enriches each prototype as a binary discriminant function with a local threshold, and optimizes...
We present a study of designing compact recognizers of handwritten Chinese characters using multiple-prototype based classifiers. A modified Quick prop algorithm is proposed to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters. Benchmark results are reported for classifiers with different...
In this paper we redefine and generalize the classic k-nearest neighbors (k-NN) voting rule in a Bayesian maximum-a-posteriori (MAP) framework. Therefore, annotated examples are used for estimating pointwise class probabilities in the feature space, thus giving rise to a new instance-based classification rule. Namely, we propose to "boost" the classic k-NN rule by inducing a strong classifier...
Nearest neighbor is one of the most successfully used techniques for performing classification and pattern recognition tasks. Its simplicity and effectiveness justify the use of this technique in certain domains but it however presents several drawbacks referring to time response, noise sensitivity and storage requirements. Several solutions have been proposed in order to alleviate these problems,...
In traditional machine learning applications, only labeled data is used to train the classifier. Labeled data are difficult, expensive, time-consuming and require human experts to be obtained in several real applications. Semi-supervised learning address this issue. Semi-supervised learning uses large amount of unlabeled data, combined with the labeled data, to build better classifiers. The semi-supervised...
This paper introduces a novel instance-based one-class classification method for novelty detection in time series based on its states transition. The main feature of our work is to generate an efficient method which automatically finds the parameters (whose yields the best model) according with the quality of the discovered time series states and the validation error. This method involves clustering...
In many real world data applications, objects may have missing attributes. Conventional techniques used to classify this kind of data are represented in a feature space. However, usually they need imputation methods and/or changing the classifiers. In this paper, we propose two classification alternatives based on dissimilarities. These techniques promise to be appealing for solving the problem of...
Text classification is the key technology for topic tracking, and vector space model (VSM) is one of the most simple and effective models for topics representation. On the basis of 2 information gain algorithm and chi square ιY in VSM, we have studied how feature selection algorithm and feature dimension in VSM affect topic tracking. And then we get the variation law that they affect topic tracking,...
Outdoor sport practitioners can improve greatly their results if they train at the right intensity. Nevertheless, in common training systems the athlete's performance is used for evaluation at the end of the exercises, and the sensed data is incomplete because only human biometrics are analyzed. These systems do not consider environmental conditions, which may have direct influence on athlete's performance...
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