Text categorization – the assignment of natural language texts to one or more predefined categories based on their content – is an important component in many information organization and management tasks. We compare the effectiveness of five different automatic learning algorithms for text categorization in terms of learning speed, realtime classification speed, and classification accuracy. We also examine training set size, and alternative document representations. Very accurate text classifiers can be learned automatically from training examples. Linear Support Vector Machines (SVMs) are particularly promising because they are very accurate, quick to train, and quick to evaluate. 1.1 Keywords Text categorization, classification, support vector machines, machine learning, information management.

Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propose a method to approach this problem by trying to estimate a function f which is positive on S and negative on the complement. The functional form of f is given by a kernel expansion in terms of a potentially small subset of the training data; it is regularized by controlling the length of the weight vector in an associated feature space. The expansion coefficients are found by solving a quadratic programming problem, which we do by carrying out sequential optimization over pairs of input patterns. We also provide a preliminary theoretical analysis of the statistical performance of our algorithm. The algorithm is a natural extension of the support vector algorithm to the case of unlabelled d...

Recent debate has centered on the relative promise of focusing user-interface research on developing new metaphors and tools that enhance users' abilities to directly manipulate objects versus directing effort toward developing interface agents that provide automation. In this paper, we review principles that show promise for allowing engineers to enhance human---computer interaction through an elegant coupling of automated services with direct manipulation. Key ideas will be highlighted in terms of the LookOut system for scheduling and meeting management. Keywords Intelligent agents, direct manipulation, user modeling, probability, decision theory, UI design INTRODUCTION There has been debate among researchers about where great opportunities lay for innovating in the realm of human--- computer interaction [10]. One group of researchers has expressed enthusiasm for the development and application of new kinds of automated services, often referred to as interface "agents." The effo...

this article with the regression case. To explain this, we will introduce a suitable definition of a margin that is maximized in both cases

Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. The goal of this tutorial is to provide an intuitive explanation of SVMs from a geometric perspective. The classification problem is used to investigate the basic concepts behind SVMs and to examine their strengths and weaknesses from a data mining perspective. While this overview is not comprehensive, it does provide resources for those interested in further exploring SVMs.

Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) problem. This paper proposes an algorithm for training SVMs: Sequential Minimal Optimization,or SMO. SMO breaks the large QP problem into a series of smallest possible QP problems which are analytically solvable. Thus, SMO does not require a numerical QP library. SMO's computation time is dominated by evaluation of the kernel, hence kernel optimizations substantially quicken SMO. For the MNIST database, SMO is 1.7 times as fast as PCG chunking; while for the UCI Adult database and linear SVMs, SMO can be 1500 times faster than the PCG chunking algorithm.

We demonstrate that it is possible to perform automatic sentiment classification in the very noisy domain of customer feedback data. We show that by using large feature vectors in combination with feature reduction, we can train linear support vector machines that achieve high classification accuracy on data that present classification challenges even for a human annotator. We also show that, surprisingly, the addition of deep linguistic analysis features to a set of surface level word n-gram features contributes consistently to classification accuracy in this domain. 1

The identification of authorship falls into the category of style classification, an interesting sub-field of text categorization that deals with properties of the form of linguistic expression as opposed to the content of a text. Various feature sets and classification methods have been proposed in the literature, geared towards abstracting away from the content of a text, and focusing on its stylistic properties. We demonstrate that in a realistically difficult authorship attribution scenario, deep linguistic analysis features such as context free production frequencies and semantic relationship frequencies achieve significant error reduction over more commonly used “shallow ” features such as function word frequencies and part of speech trigrams. Modern machine learning techniques like support vector machines allow us to explore large feature vectors, combining these different feature sets to achieve high classification accuracy in style-based tasks.

: Kernel Methods have become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. They exhibit good generalisation performance on many real-life datasets and the approach is properly motivated theoretically. There are relatively few free parameters to adjust and the architecture of the learning machine does not need to be found by experimentation. In this tutorial we survey this subject with a principal focus on the most well-known models based on kernel substitution, namely, Support Vector Machines. 1 Introduction. Support Vector Machines (SVMs) have been successfully applied to a number of applications ranging from particle identification, face identification and text categorisation to engine knock detection, bioinformatics and database marketing [9]. The approach is systematic and properly motivated by statistical learning theory [42]. Training involves optimisation of a convex cost function: there are no false local mi...

Abstract. In this paper we investigate the possibility of evaluating MT quality and fluency at the sentence level in the absence of reference translations. We measure the correlation between automatically-generated scores and human judgments, and we evaluate the performance of our system when used as a classifier for identifying highly dysfluent and illformed sentences. We show that we can substantially improve on the correlation between language model perplexity scores and human judgment by combining these perplexity scores with class probabilities from a machine-learned classifier. The classifier uses linguistic features and has been trained to distinguish human translations from machine translations. We show that this approach also performs well in identifying dysfluent sentences. 1.