Recently, (Blanchet, Kang, and Murhy 2016) showed that several machine learning algorithms, such as square-root Lasso, Support Vector Machines, and regularized logistic regression, among many others, can be represented exactly as distributionally robust optimization (DRO) problems. The distributional uncertainty is defined as a neighborhood centered at the empirical distribution. We propose a methodology which learns such neighborhood in a natural data-driven way. We show rigorously that our framework encompasses adaptive regularization as a particular case. Moreover, we demonstrate empirically that our proposed methodology is able to improve upon a wide range of popular machine learning estimators.

Support Vector Data Description (SVDD) provides a useful approach to construct a description of multivariate data for single-class classification and outlier detection with various practical applications. Gaussian kernel used in SVDD formulation allows flexible data description defined by observations designated as support vectors. The data boundary of such description is non-spherical and conforms to the geometric features of the data. By varying the Gaussian kernel bandwidth parameter, the SVDD-generated boundary can be made either smoother (more spherical) or tighter/jagged. The former case may lead to under-fitting, whereas the latter may result in overfitting. Peak criterion has been proposed to select an optimal value of the kernel bandwidth to strike the balance between the data boundary smoothness and its ability to capture the general geometric shape of the data. Peak criterion involves training SVDD at various values of the kernel bandwidth parameter. When training datasets are large, the time required to obtain the optimal value of the Gaussian kernel bandwidth parameter according to Peak method can become prohibitively large. This paper proposes an extension of Peak method for the case of large data. The proposed method gives good results when applied to several datasets. Two existing alternative methods of computing the Gaussian kernel bandwidth parameter (Coefficient of Variation and Distance to the Farthest Neighbor) were modified to allow comparison with the proposed method on convergence. Empirical comparison demonstrates the advantage of the proposed method.

Text mining (deriving information from text) is a wide field which has gained popularity with the huge text data being generated. Automation of a number of applications like sentiment analysis, document classification, topic classification, text summarization, machine translation, etc has been done using machine learning models. Spam filtering is a beginner's example of document classification task which involves classifying an email as spam or non-spam (a.k.a. Spam box in your Gmail account is the best example of this. So lets get started in building a spam filter on a publicly available mail corpus.

Whether you are a newcomer to machine learning, a newbie to specific algorithms or concepts, or a seasoned ML vet looking for a once-over of an algorithm you haven't seen or used in a while, these short and to-the-point tutorials may provide the assistance you are looking for. Each of these posts concisely covers a single, specific machine learning concept. Support Vector Machines remain a popular and time-tested classification algorithm. This post provides a high-level concise technical overview of their functionality. A wide array of clustering techniques are in use today.

In this paper, we investigate the authorship of several short historical texts that are written by ten ancient Arabic travelers: this Arabic dataset, which was collected by the authors in 2011, and called AAAT (Authorship attribution of Ancient Arabic Texts) corpus, is considered as a reference dataset in Arabic. Several experiments of authorship attribution are conducted by using different features namely: characters, character n-grams, and lexical features such as words, word n-grams, and rare words. On the other hand, different classifiers are employed, such as: statistical distances, Multi Layer Percep-tron (MLP), Support Vector Machines (SVM) and Linear Regression (LR). In this investigation, a new fusion technique is proposed to enhance the overall performances of the classifiers: it is called Score Based Fusion (SBF). Results show good attribution performances with an optimal score between 80% and 90% of good authorship attribution. The proposed fusion technique raised this score to 100% of good authorship attribution. Moreover, this comparative survey has revealed interesting results concerning the Arabic language and more particularly with short texts.

Word Sense Disambiguation (WSD) is a classification task that consists of determining which of the senses of an ambiguous word is activated in a specific context. Research in this field has primarily concentrated on investigating English and a few other well-resourced languages. Recently, studies done on a corpus of Old English (Wunderlich 2015) showed that, even with limited resources, it is still possible to approach the problem of WSD. In this paper, a WSD system has been developed for the Low Resource Language (LRL) Venetan, which has recently received some attention from the Natural Language Processing (NLP) community. Our main contributions are twofold: first, we select and annotate a corpus for Venetan, considering two words (one abstract and one concrete term) and using two levels of annotation (fine- and coarse-grained), reporting on annotator agreement. Second, we report results of proof-of-concept experiments of supervised WSD performed with Support Vector Machines on this corpus. To our knowledge, our work is the first time that WSD for a European Dialect like Venetan has been studied.

Distributed Denial of Service (DDoS) attacks are a major threat to Internet security, with their use continuing to grow. Attackers are finding more sophisticated methods to attack servers. A lot of defense mechanisms have been proposed for DDoS attacks at IP and TCP layers. Those methods will not work well for application layer DDoS attacks that utilize legitimate application layer requests to overwhelm a webserver. These attacks look legitimate in both packets and protocol characteristics, which makes them harder to detect. In this paper, we propose an anomaly detection method to detect application layer DDoS attacks. We take a text mining approach to extract features which represent a user’s HTTP request sequence using bigrams. We apply the one class Support Vector Machine (SVM) algorithm on the extracted features from normal users’ HTTP request sequences. The one class SVM labels any newly seen instance that deviates from the normal, trained model as an application layer DDoS instance. We apply our experimental analysis on real web server logs collected from a student resource website. Three different variants of HTTP GET flood attacks are implemented on our server, generated via penetration testing. Our results show that the proposed method is able to detect application layer DDoS attacks with very good performance results.

The E-learning course starts by refreshing the basic concepts of the analytics process model: data preprocessing, analytics and post processing. We then discuss decision trees and ensemble methods (bagging, boosting, random forests), neural networks, support vector machines (SVMs), Bayesian networks, survival analysis, social networks, monitoring and backtesting analytical models. Throughout the course, we extensively refer to our industry and research experience. The E-learning course consists of more than 20 hours of movies, each 5 minutes on average. Quizzes are included to facilitate the understanding of the material.

Two-Class Support Vector Machine is used to create a model that is based on the Support Vector Machine Algorithm.The classifier that this module initializes is useful for predicting between two possible outcomes that depend on continuous or categorical predictor variables. This model is a supervised learning method and therefore, requires a dataset which includes a labeled column. You can train the model by providing the model and the tagged dataset as an input to Train Model or Tune Model Hyperparameters. The trained model can then be used to predict values for the new input examples. Support Vector Machines (SVMs) are supervised learning models that analyze data and recognize patterns.

Support vector machines (SVMs) are an important tool in modern data analysis. Traditionally, support vector machines have been fitted via quadratic programming, either using purpose-built or off-the-shelf algorithms. We present an alternative approach to SVM fitting via the majorization--minimization (MM) paradigm. Algorithms that are derived via MM algorithm constructions can be shown to monotonically decrease their objectives at each iteration, as well as be globally convergent to stationary points. We demonstrate the construction of iteratively-reweighted least-squares (IRLS) algorithms, via the MM paradigm, for SVM risk minimization problems involving the hinge, least-square, squared-hinge, and logistic losses, and 1-norm, 2-norm, and elastic net penalizations. Successful implementations of our algorithms are presented via some numerical examples.