We congratulate the authors for a very interesting article. The key contribution of this paper, as we see it and as suggested in the title, is the introduction of latent parameters to carry out Bayesian analysis with support vector machines. The basic identities (4) and (6) are particularly useful in this regard, which enable one to overcome much of the complexities of a non smooth loss resulti...

For many years now, there is a growing interest around ROC curve for characterizing machine learning performances. This is particularly due to the fact that in real-world problems misclassification costs are not known and thus, ROC curve and related metrics such as the Area Under ROC curve (AUC) can be a more meaningful performance measures. In this paper, we propose a SVMs based algorithm for ...

The relationships between geological features and rockmass behaviors under complex geological environments were investigated based on multiple intelligence classifiers. Random forest, support vector machine, bayes’ classifier, fisher’s classifier, logistic regression, and neural networks were used to establish models for evaluating the rockmass stability of slope. Samples of both circular failu...

In Hungarian intonation research the goal of a common framework developed by Varga (2002; [1]) is to categorize the intonation within the domain of accent groups by character contours. We propose a linear parameterization of a subset of these contours derived from polynomial stylization. These parameters were used to train classification trees and support vector machines for contour prediction....

Training a support vector machine (SVM) leads to a quadratic optimization problem with bound constraints and one linear equality constraint. Despite the fact that this type of problem is well understood, there are many issues to be considered in designing an SVM learner. In particular, for large learning tasks with many training examples, off-the-shelf optimization techniques for general quadra...

Feature selection aims to reduce the dimensionality of patterns for classificatory analysis by selecting the most informative instead of irrelevant and/or redundant features. In this paper we propose a novel feature selection measure based on mutual information and takes into consideration the interaction between features. The proposed measure is used to determine relevant features from the ori...

In this paper, we propose a new method to choose the effective samples for support vector machines (SVM) training in audio classification task. The objective is to reduce the training time of SVM by choosing effective examples from the training set of binary classes. We test the performances of our new method on a dataset composed of about 6-hour audio data which illustrate that the computation...

In this paper, we address the tasks of classifying the posts of mailing lists and forums, and retrieving relevant information from the same (describing our participation in the FIRE 2010 evaluation sub-tasks for the same). We approach the classification problem in two ways. In the first method, we pose it as a sequence labeling task using conditional random fields (CRF), where the entire thread...

Appropriate and acceptable prediction of bed load being carried by streams is vitally important for water resources quantity and quality studies. Although measuring the rate of bed load in situ is the most consistent method, it is very expensive and cannot be conducted for as many streams as the measurement of suspended sediment load. Therefore, in this study the role of suspended load on bedlo...

Machine learning algorithm can significantly help in solving the healthcare problems by developing classifier systems that can assist physicians in diagnosing and predicting diseases in early stages. However, extracting knowledge from medical data is challenging as this data may be heterogeneous, unorganized, and high dimensional and may contain noise and outliers. Most appropriate method can b...