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E-learning has witnessed a great interest from the part of corporations, educational institutions and individuals alike. As an education pattern, e-learning systems have become more and more popular. It commonly refers to teaching efforts propagated through the use of computers in a bid to impart knowledge in a non traditional classroom environment. As a prerequisite for an effective development of...
Development of a feature ranking method based upon the discriminative power of features and unbiased towards classifiers is of interest. We have studied a consensus feature ranking method, based on multiple classifiers, and have shown its superiority to well known statistical ranking methods. In a target environment such as a medical dataset, missing values and an unbalanced distribution of data must...
Background: The majority of software faults are present in small number of modules, therefore accurate prediction of fault-prone modules helps improve software quality by focusing testing efforts on a subset of modules. Aims: This paper evaluates the use of the faults-slip-through (FST) metric as a potential predictor of fault-prone modules. Rather than predicting the fault-prone modules for the complete...
This paper presents a distributed Support Vector Machine (SVM) algorithm in order to detect malicious software (malware) on a network of mobile devices. The light-weight system monitors mobile user activity in a distributed and privacy-preserving way using a statistical classification model which is evolved by training with examples of both normal usage patterns and unusual behavior. The system is...
In recent years, statistical learning theory ( for short SLT)and support vector machine(for short SVM) has become an international field of machine learning new hotspot. Decision tree classification learning algorithm is one of the most widely used and very practical inductive reasoning method, In machine learning, data mining, signal processing, intelligent control, artificial intelligence area has...
Transductive inference based on support vector machine is a new research region in statistical learning theory. An improved algorithm is proposed in this paper, which overcome the disadvantages of studying process complexity and slow in the progressive transductive support vector machine learning algorithm. The algorithm optimized the samples which near the support vector only, and large number of...
In this paper, B-placenta image is classified automatically using support vector machine based on feature extraction. Firstly, artificial selected region of interest (ROI) is regarded as the object of feature extraction. Then traditional gray-scale statistical analysis is used to extract the characteristic parameters of B-placenta image as the basis data for the placenta classification. The binary...
Our goal is to automatically identify which species of bird is present in an audio recording using supervised learning. Devising effective algorithms for bird species classification is a preliminary step toward extracting useful ecological data from recordings collected in the field. We propose a probabilistic model for audio features within a short interval of time, then derive its Bayes risk-minimizing...
The mind speller is a brain-computer interface which enables subjects to spell text on a computer screen by detecting P300 event-related potentials in their electroencephalograms. This BCI application is of particular interest for disabled patients who have lost all means of verbal and motor communication. We report on the implementation of a feature extraction procedure on a new ultra low-power 8-channel...
Lung cancer patients who receive radiotherapy as part of their treatment are at risk radiation-induced lung injury known as radiation pneumonitis (RP). RP is a potentially fatal side effect to treatment. Hence, new methods are needed to guide physicians to prescribe targeted therapy dosage to patients at high risk of RP. Several predictive models based on traditional statistical methods and machine...
In this paper, respiratory rate is extracted using signal processing and machine learning methods from electrical impedance, measured across arm. Two pairs of electrodes have been used along the arm, one for injecting the current, and one for sensing the voltage. After filtering, the frequency components and other signal features have been extracted using Short Time Fourier Transform (STFT). Then...
Text classification plays an important role in information extraction and summarization, text retrieval, and question-answering. The discriminative multinomial naive Bayes classifier has been a focus of research in the field of text classification. This paper increases the accuracy of discriminative multinomial Bayesian classifier with the usage of the feature selection technique that evaluates the...
This paper presents a new classification method for online handwritten Chinese character recognition (HCCR). For classification, a similarity measure is established via statistical technique which calculates the coefficient of determination (Rp2) for 2-dimensional unreplicated linear functional relationship (MULFR) model between the trajectory pattern of input character and character in database,...
Various classification methods have been used to predict the class of tissue samples based on gene expression data. prediction analysis for microarrays (PAM) is one of the top classifiers that has been extensively used for cancer classification. In this paper a novel method of combining expression data from gene pairs is used to improve the overall accuracy of PAM. Recent studies suggest that deregulation...
Support vector machine (SVM) is a novel machine learning method based on statistical learning theory (SLT). SVM is powerful for the problem with small samples, non linear and high dimension. A multi-class SVM classifier is applied to predict the coal and gas outburst in the paper. In this model, the dominant factors are the input vectors and the degree of outburst danger is divided into four types:...
Process control analysis system based on data warehouse (DWPCS) is introduced in this paper, which integrates the theory of SPC and DW. It firstly introduces the general framework of DWPCS and then gives an example of building subject data set that contains the relative data of control parameters. Lastly it detailedly explains the process of selecting related variables by statistical analysis methods,...
Support Vector Machine (SVM) is based on statistical learning theory which developed from the common machine learning. It is an effective tool to deal with limited samples. This paper proposes a model of the dissolved gas analysis (DGA) of transformer based on Multi-class SVM. Firstly, with the combination of SVM multi-class classification methods one-versus-rest (1-v-r) and one-versus-one (1-v-1),...
Bug assignment is an important step in bug life-cycle management. In large projects, this task would consume a substantial amount of human effort. To compare with the previous studies on automatic bug assignment in FOSS (free/open source software) projects, we conduct a case study on a proprietary software project in China. Our study consists of two experiments of automatic bug assignment, using Chinese...
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