Decision support system using data mining to find decision knowledge is called Intelligent Decision Support System(IDSS). Rough set as a data mining method commonly is used to find classification knowledge in IDSS. But the classic data mining based on rough set is short in dealing with the blank value data or data with the character of blurring and randomicity. Such data is called as imperfect ...

Parameter selection is one of the important steps involved in any model fitting. In this paper we have used Uniform Design Tables to choose the parameters for PSVM and SVM to classify the data. UD is one of the efficient space filling designs, which spreads the combination of parameters in the space uniformly scattered and generalizes the performance of the model efficiently. This paper compare...

An image classification method based on Support Vector Machine (SVM) is proposed on hyperspectral and 3K DSM data. To obtain training data we applied an automatic method relating to four classes namely; building, grass, tree, and ground pixels. First, some initial segments regarding to building, tree, grass, and ground pixels are produced using different feature descriptors. The feature descrip...

Data mining methods based on support vector machine are attractive to address the curse of dimensionality. The Kernel mapping contributes a unifying frame work for most of the commonly employed models to get the linear planes in the higher dimensional space. In this paper, we prove this approach enhances the accuracy of diabetes data set. We further refine the results with parameter tuning for ...

Recorded disturbances are often evaluated manually by specialists. However, a lot of time could be saved if a majority of the recorded information could be classified automatically. This paper proposes a novel classification system based on the Support Vector Machine method for automatic classification of seven types of voltage disturbances. The performance of the classification system was inve...

Support vector machine is a popular method in machine learning. Incremental support vector machine algorithm is ideal selection in the face of large learning data set. In this paper a new incremental support vector machine learning algorithm is proposed to improve efficiency of large scale data processing. The model of this incremental learning algorithm is similar to the standard support vecto...

This project uses Support Vector Machines (SVM) for a gender classification problem. SVM technique was applied for the training data and test data created from the given dataset GENDER. It is a two-class problem where the classes are not linearly separable. Kernel methods for mapping the data onto a higher dimensional space were used. The performance of the two-class classifier based on SVM was...

Raw cow milk has short supply market in summer and over supply in winter, which causes consumers and dairy industry concern about the quality of raw milk whether is adulated with reconstituted milk (powdered milk). This study prepared 307 raw cow milk samples with various adulteration ratios 0%, 2%, 5%, 10%, 20%, 30%, 50%, 75%, and 100% of powdered milk. Least square support vector machine (LS-...

This paper presents results of applying a machine learning technique, the Support Vector Machine (SVM), to the astronomical problem of matching the Infra-Red Astronomical Satellite (IRAS) and Sloan Digital Sky Survey (SDSS) object catalogues. In this study, the IRAS catalogue has much larger positional uncertainties than those of the SDSS. A model was constructed by applying the supervised lear...

In structured prediction, it is standard procedure to discriminatively train a single model that is then used to make a single prediction for each input. This practice is simple but risky in many ways. For instance, models are often designed with tractability rather than faithfulness in mind. To hedge against such model misspecification, it may be useful to train multiple models that all are a ...