Benzo[c]phenanthridine (BCP) derivatives were identified as topoisomerase I (TOP-I) targeting agents with pronounced antitumor activity. In this study, a support vector machine model was performed on a series of 73 analogues to classify BCP derivatives according to TOP-I inhibitory activity. The best SVM model with total accuracy of 93% for training set was achieved using a set of 7 descriptors...

We address the problem of model selection for Support Vector Machine (SVM) classification. For fixed functional form of the kernel, model selection amounts to tuning kernel parameters and the slack penalty coefficient C. We begin by reviewing a recently developed probabilistic framework for SVM classification. An extension to the case of SVMs with quadratic slack penalties is given and a simple...

The accuracy levels achieved by state-of-the-art Speaker Verification systems are high enough for the technology to be used in real-life applications. Unfortunately, the transfer from the lab to the field is not as straight-forward as could be: the best performing systems can be computationally expensive to run and need large speaker model footprints. In this paper, we compare two speaker verif...

The support vector machine (SVM) shows many unique advantages in solving the small sample, nonlinear and high dimensional pattern recognition problems, and it is very suitable to solve the classification problem in motor imagery EEG. For SVM using radial basis function (RBF) kernel, two parameters had to be selected beforehand: the trade-off parameter C and the kernel parameter σ. The tradition...

In this chapter, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM). Secondly, we present approximate solutions for two computational problems arising in GP and SVM. The rst one is the calculation of the posterior mean for GP classiiers using a `naive' mean eld approach. The second one is a leave-one-out estimator for the generalization...

A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitation. These connections are generalized to cost-sensitive classification, in a...

This paper proposes machine algorithm to grade (Premium, Grade A, Grade B and Grade C) the rice kernels using Multi-Class SVM. Maximum Variance method was applied to extract the rice kernels from background, then, after the chalk has been extracted from rice. The percentage of Head rice, broken rice and Brewers in rice samples were determined using ten geometric features. Multi-Class SVM classi...

The agricultural data classification is a hot topic in the field of precision agriculture. Support vector machine (SVM) is a kind of structural risk minimization based learning algorithms. As a popular machine learning algorithm, SVM has been widely used in many fields such as information retrieval and text classification in the last decade. In this paper, SVM is introduced to classify the agri...

Ranking algorithms are often introduced with the aim of automatically personalising search results. However, most ranking algorithms developed in the machine learning community rely on a careful choice of some regularisation parameter. Building upon work on the regularisation path for kernel methods, we propose a parameter selection algorithm for ranking SVM. Empirical results are promising.

This paper explores utilization of information from social networks in making automatic movie recommendations. Implementations of three different algorithms (SVM, Clustering, and Ranking SVM) are implemented and evaluated. The general approach utilizes a large collection of Facebook profile information as training set in order to generate a list of movie recommendations for a particular user (c...