The kernel support vector machine (SVM) is one of the most widely used classification methods; however, the amount of computation required becomes the bottleneck when facing millions of samples. In this paper, we propose and analyze a novel divide-and-conquer solver for kernel SVMs (DC-SVM). In the division step, we partition the kernel SVM problem into smaller subproblems by clustering the dat...

Our recent work on large-scale learning using b-bit minwise hashing [21, 22] was tested on the webspam dataset (about 24 GB in LibSVM format), which may be way too small compared to real datasets used in industry. Since we could not access the proprietary dataset used in [31] for testing the Vowpal Wabbit (VW) hashing algorithm, in this paper we present an experimental study based on the expand...

“Hype or Hallelujah?” is the provocative title used by Bennett & Campbell (2000) in an overview of Support Vector Machines (SVM). SVMs are currently a hot topic in the machine learning community, creating a similar enthusiasm at the moment as Artificial Neural Networks used to do before. Far from being a panacea, SVMs yet represent a powerful technique for general (nonlinear) classification, re...

We have developed a package which can effectively evaluate gene function in discriminating biological samples of different types. Sixteen gene selection algorithms are included in this package. We write eight of them in MATLAB, and also provide MATLAB interface for a popular software rankgene1.1 which integrates other eight typical methods. Additionally, a MATLAB interface for two well-known cl...

One of the main drawbacks of Support Vector Machines (SVM) is their high computational cost for large data sets. We propose the use of the Leader algorithm as a preprocessing procedure for SVM with large data sets, so that the obtained leaders are used as the training set for the SVM. The result is an algorithm where the Leader algorithm allows to construct a sample of the data set whose granul...

We have prepared multispectral image database of skin tumor diagnosis. All images have been labeled with two classes tumor and healthy tissues. We have extracted pixel signatures with their spectral data and class assigning, thus obtained train dataset. Next we have used and evaluated the supervised learning techniques for the purpose of automatic tumor detection. We have tested Naive Bayes, KN...

We introduce the OneClassMaxMinOver (OMMO) algorithm for the problem of one-class support vector classification. The algorithm is extremely simple and therefore a convenient choice for practitioners. We prove that in the hard-margin case the algorithm converges with O(1/ √ t) to the maximum margin solution of the support vector approach for one-class classification introduced by Schölkopf et al...

We present the system we built for participating in the PAN-2016 Author Profiling Task [9]. The task asked to predict the gender and the age group of a person given several samples of his/her writing, and it was offered for three different languages: English, Spanish, and Dutch. We participated in both subtasks, for all three languages. Our approach focused on extracting genre-agnostic features...

“Hype or Hallelujah?” is the provocative title used by Bennett & Campbell (2000) in an overview of Support Vector Machines (SVM). SVMs are currently a hot topic in the machine learning community, creating a similar enthusiasm at the moment as Artificial Neural Networks used to do before. Far from being a panacea, SVMs yet represent a powerful technique for general (nonlinear) classification, re...