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A multi-kernel Support Vector Machine model, called Hierarchical Support Vector Regression (HSVR), is proposed here. This is a self-organizing (by growing) multiscale version of a Support Vector Regression (SVR) model. It is constituted of hierarchical layers, each containing a standard SVR with Gaussian kernel, at decreasing scales. HSVR have been applied to a noisy synthetic dataset. The results...
In this research, we have extended the use of Kernel Dimensionality Reduction (KDR) in the context of semi supervised learning in particular for micro-array DNA clustering application. We have proposed a new model call K-means-KDR for survival analysis which we aimed to improve the genes classification performance and study the dimension of effective subspaces in cancer patient survival analysis....
The great achievements have been approached in the development of support vector machine (SVM). It has been successfully used for solving classification and regression problems. This paper aims at proposing two algorithms based on SVC and SVR which are two applications of SVM in the fields of classification and regression, to handle both nominal and numerical missing values. Two experiments are conducted...
The application of feature ranking to software engineering datasets is rare at best. In this study, we consider wrapper-based feature ranking where nine performance metrics aided by a particular learner are evaluated. We consider five learners and take two different approaches, each in conjunction with one of two different methodologies: 3-fold Cross-Validation (CV) and 3-fold Cross-Validation Risk...
The performance and regression precision of weak learners (accuracies should be greater than 0.5) for pattern recognition and forecasting can be upgraded using AdaBoost algorithm. Support vector machine (SVM) is a state-of-the-art learning machines and have been widely used in pattern recognition area since 90's of 20th contrary, however the performance of SVM is not stable and easily influenced due...
According to the theory of SVM, the models for the assessment of air environment quality were built and analyzed based on support vector classification and support vector regression. The conclusions are that the SVM is an easy and accurate method for the assessment of air environment quality and the model for the assessment of air environment quality is more accurate based on support vector classification...
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed general approach to formalizing such problems, known as learning...
How to extract rules from trained SVMs has become an important preprocessing technique for data mining, pattern classification, and so on. There are two key problems required to be solved in the SVM based classification rule extraction, i.e. the attribute selection and the discretization to continuous attributes. In this paper, the differential characteristic of SVR (Support vector regression) is...
When the training subset of a support vector machine contains fuzzy information, the support vector machine won't work. A method for calculating fuzzy linear separable support vector classifier is discussed. With the given confidence level, convert the fuzzy classification problem into finding the fuzzy chance constrained programming, and establish a solution finding theory with fuzzy chance constrained...
By comparing performance of common kernels and wavelet kernels in classification, criterion of effective kernel for support vector classifier is concluded, thereby a tight support kernel is constructed by smoothing Shannon scaling function in Fourier domain and combining with spline function. Experiment results indicate that the proposed kernel has faster training speed and higher accuracy than Gaussian...
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