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Feature learning from unlabeled times series data is an important component in data analysis. Shaplets are discriminative sub-sequence of time series that can best predict target variable. Therefore, shaplets discovery is very important for analysis of time-series. Recently, based on optimization model, a novel approach has been proposed to learning shaplets. To make shaplets learning model more robust,...
With the introduction of trust mechanism into the wireless ad hoc network, security can be greatly improved, because trust information is shared among all nodes in the network. However, such a mechanism also has the potential vulnerability if the so-called 'big-mouth' nodes broadcast wrong trust information. Some measures have to be taken to filter the received trust information before it is fed to...
This paper discusses the second-order mining of the results of data mining. There is still gap between the knowledge which can direct the operation of company and the knowledge got from data mining. We take the knowledge from data mining as primary knowledge and the knowledge from second-order data mining as intelligent knowledge. We discuss the importance of intelligent knowledge and the way to find...
Linear Support Vector Machines(SVMs) have broad application in supervised classification problem with high dimensional feature space, such as text classification, word sense disambiguation, email spam detection and etc.. Considering the large volume of available training data, efficient training algorithm for linear SVMs draws many attention from the research community in recent years. Cutting-plane...
The rapid development of data technology, as exemplified by data mining and Internet growth, creates a large information overload and forthcoming knowledge overload. Data mining discovers a large mount of knowledge, but not all of the knowledge is useful. Meanwhile the useful knowledge will also become un-useful as time goes by. How to manage this kind of knowledge is an urgent problem for data mining...
According to Domingospsila bias-variance decomposition framework, we study the bias-variance characteristics of the standard Multiple Criteria Linear Programming (MCLP) classification method. The experimental results show that, under Domingospsila bias-variance decomposition framework, bias is much bigger than variance, and boosting ensemble doesnpsilat behave better than bagging ensemble, and increasing...
The email has profoundly affected our ways of life. In the year 2001, many Web sites begin operating the charge emails in china. Recently the foreign Internet company serves free email with large storage capability, which threats the existence of charge email. The churn of email users is serious. This paper studied the churn of the customer using the way of data mining based on the background introduced...
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