Machine Learning and Data Mining in Pattern Recognition: 9th by Taku Onodera, Tetsuo Shibuya (auth.), Petra Perner (eds.)

By Taku Onodera, Tetsuo Shibuya (auth.), Petra Perner (eds.)

This e-book constitutes the refereed complaints of the ninth foreign convention on computer studying and information Mining in development attractiveness, MLDM 2013, held in big apple, united states in July 2013. The fifty one revised complete papers offered have been conscientiously reviewed and chosen from 212 submissions. The papers hide the subjects starting from theoretical themes for category, clustering, organization rule and trend mining to precise information mining tools for the several multimedia information kinds comparable to picture mining, textual content mining, video mining and internet mining.

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Additional resources for Machine Learning and Data Mining in Pattern Recognition: 9th International Conference, MLDM 2013, New York, NY, USA, July 19-25, 2013. Proceedings

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It is difficult to decide what to specialize and when to stop. In addition, as stated in [6], searching by specialization is usually considered as one of the most time-consuming methods. Hence, it causes the needs to tradeoff between the accuracy and speed of the algorithm. Moreover, another problem that was sought in RULES family is the way it handles incomplete data. Examples that have a missing class are either neglected or filled based on other examples in the training set. When the example is neglected it is possible to lose important information and decrease the performance of the algorithm.

Following that, the algorithm is tested and its empirical result that compares it with other covering algorithms is explained and discussed. Finally, the paper is concluded and future work is presented. 2 Background This section explains the background needed to understand the proposed algorithm, including RULES family and Transfer Learning. 1 RULe Extraction System - RULES RULES family is one of the covering algorithms that directly induces rules from the training set based on the concept of separate and conquer.

So a parameter, w, was introduced. If a rule does not retrieve at least w (stock, week) pairs in the training data set, the fitness is 0. This encourages the GA to locate rules that apply to larger sets of data and not focus in on global optima. These modifications to the standard fitness function allow the algorithm to locate better rules. Unlike other GAs DSGA does not use the fitness function for selection. It uses a shared fitness function. The fitness function value is altered to encourage exploration in new areas of the domain.

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