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This paper presents a novel approach for cursive character recognition by using multiple feature extraction algorithms and a classifier ensemble. Several feature extraction techniques, using different approaches, are extracted and evaluated. Two techniques, Modified Edge Maps and Multi Zoning, are proposed. The former one presents the best overall result.(More)
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(More)
— It is herein proposed a handwritten digit recognition system which uses multiple feature extraction methods and classifier ensemble. The combination of the feature extraction methods is motivated by the observation that different feature extraction algorithms have a better discriminative power for some types of digits. Six features sets were extracted,(More)
One of the main problems in pattern recognition is obtaining the best set of features to represent the data. In recent years, several feature extraction algorithms have been proposed. However, due to the high degree of variability of the patterns, it is difficult to design a single representation that can capture the complex structure of the data. One(More)
—In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classi-fiers is generated from the training data. The second phase is responsible to extract the meta-features and train the meta-classifier. Five distinct sets of meta-features are proposed,(More)
In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are selected to classify a given query sample. Hence, the key issue in DES is how to estimate the competence of each classifier in a pool to select the most competent ones. In order to deal with this issue, we proposed a novel dynamic ensemble selection framework using(More)
Purpose Sorafenib is a small molecule inhibitor of multiple signaling kinases thought to contribute to the pathogenesis of many tumors including brain tumors. Clinical trials with sorafenib in primary and metastatic brain tumors are ongoing. We evaluated the plasma and cerebrospinal fluid (CSF) pharmacokinetics (PK) of sorafenib after an intravenous (IV)(More)
In Dynamic Ensemble Selection (DES), only the most competent clas-sifiers are selected to classify a given query sample. A crucial issue faced in DES is the definition of a criterion for measuring the level of competence of each base classifier. To that end, a criterion commonly used is the estimation of the competence of a base classifier using its local(More)