Outlier detection is an important task in data mining because outliers can be either useful knowledge or noise. Many statistical methods have been applied to detect outliers, but they usually assume a given distribution of data and it is difficult to deal with high dimensional data. The Statistical Learning Theory (SLT) established by Vapnik et aI. provides a new way to overcome these drawbacks...

Learning To Rank (LTR) techniques aim to learn an effective document ranking function by combining several document features. While the function learned may be uniformly applied to all queries, many studies have shown that different ranking functions favour different queries, and the retrieval performance can be significantly enhanced if an appropriate ranking function is selected for each indi...

Many different ranking algorithms based on content and context have been used in web search engines to find pages based on a user query. Furthermore, to achieve better performance some new solutions combine different algorithms. In this paper we use simulated click-through data to learn how to combine many content and context features of web pages. This method is simple and practical to use wit...

A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here.  The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...

prediction the river flow discharge values are important in the surface water resources management. find an appropriate model to accurately predictionof this parameter is one of the most important ways to simulation and prediction. in this study three anfis, svm and gp models were evaluated and compared to modeling the monthly flow discharge of nazloochi river in tapik hydrometric station that ...