By Cheong Hee Park (auth.), Rajendra Prasath, T. Kathirvalavakumar (eds.)
This e-book constitutes the court cases of the 1st foreign convention on Mining Intelligence and data Exploration, MIKE 2013, held in Tamil Nadu, India on December 2013. The eighty two papers awarded have been conscientiously reviewed and chosen from 334 submissions. The papers hide the subjects reminiscent of function choice, class, clustering, photo processing, community safeguard, speech processing, desktop studying, info retrieval, recommender structures, normal language processing, language, cognition and computation and different yes difficulties in dynamical systems.
Read Online or Download Mining Intelligence and Knowledge Exploration: First International Conference, MIKE 2013, Tamil Nadu, India, December 18-20, 2013. Proceedings PDF
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Additional resources for Mining Intelligence and Knowledge Exploration: First International Conference, MIKE 2013, Tamil Nadu, India, December 18-20, 2013. Proceedings
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The first step would be to use the area under the error curve as a metric to evaluate the quality of the curves. Rank Aggregation for Filter Feature Selection in Credit Scoring 15 References 1. : Evolutionary-based feature selection approaches with new criteria for data mining: A case study of credit approval data. Expert Syst. Appl. 36, 5900–5908 (2009) 2. : The effect of principal component analysis on machine learning accuracy with high-dimensional spectral data. -Based Syst. 19, 363–370 (2006) 3.
A variety of approaches have been developed for this problem. Existing methods can be categorized into two main themes. One focuses on finding appropriate features for quantifying the authors’ writing style, and the other focuses on developing efficient and effective techniques to perform the classification task. There is a body of literature examining the effects of different features. The use of function words could date back about half a century ago [22]. Since then, various features have been proposed for modeling writing styles.
Palmprint samples from different individuals having distinctive texture features can be seen from Fig 3. Texture on the palm can be clearly described by both, features at the local level and at the global level. Global palmprint features possess the following characteristics: (a) insensitive to noise; (b) insensitive to shift changes; (c) easy to compute; and 4) have high convergence within the group and good dispersion between groups [4]. Global features can lower the effect raised by local noises thus supplementing each other.