Support vector machines-machine learning
Efficient classification of cancer using support vector machines and modified extreme learning machine based on analysis of variance features
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Problem statement: The primary objective is to propose efficient cancer classification techniques which provide reliable and significant classification accuracy. To achieve this primary research goal is to find the smallest set of genes that can ensure high accuracy in
Optimization, support vector machines , and machine learning
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Page 1. Optimization, Support Vector Machines , and Machine Learning Chih-Jen Lin Department of Computer Science National Taiwan University Talk at DIS, University of Rome and IASI, CNR, September . p.1/121 Page 2. Outline Introduction to machine learning and support vector
Humans out- learning the machine : support vector machines applied to fMRI of human motor learning
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Figure 1. Box plots and connecting black line show distribution and median of button rate. Green line (right axis) shows ave. estimation error for SVR model trained on run 1. Humans Out- Learning the Machine : Support Vector Machines Applied to fMRI of Human Motor Learning SM LaConte1
Support Vector Machines in the Machine Learning Classifier for a Texas Holdem Poker Bot
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This design project explores a fascinating application of artificial intelligence and machine learning to one of todays most popular mental challenges: Texas Holdem poker. Recently, IBMs Deep Blue computer chess program managed to defeat the grandmaster Gary
Alzheimers Disease and Support Vector Machines : An Introduction to Machine Learning
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An introduction to machine learning is provided, wherein the basic theory of a major concept, support vector machines , is developed. This idea is then applied to data from two studies on Alzheimers disease. The results indicate that support vector machines can be an
EARNING MOVEMENT PREDICTION USING MACHINE LEARNING – SUPPORT VECTOR MACHINES (SVM)
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The prediction of earnings movement is used to evaluate corporate performance and make investment decisions. This study presents a detailed model for predicting the movement of company future earnings using Support Vector Machines (SVM) technique and
Automatic Traffic Scene Analysis Using Supervised Machine Learning Algorithms-Backpropagation Neural Networks and Support Vector Machines
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Automatic traffic scene analysis which has been used for real-time on-road vehicle detection system is essential to many areas of ITS (Intelligent Transport Systems). In order to improve the detection time and accuracy of detection performance, various image processing
Fuzzy support vector machines
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A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate
Semi-supervised support vector machines
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We introduce a semi-supervised support vector machine (S3yM) method. Given a training set of labeled data and a working set of unlabeled data, S3YM constructs a support vector machine using both the training and working sets. We use S3YM to solve the transduction
A comparison of methods for multiclass support vector machines
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Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by
Support vector machines for classification and regression
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The problem of empirical data modelling is germane to many engineering applications. In empirical data modelling a process of induction is used to build up a model of the system, from which it is hoped to deduce responses of the system that have yet to be observed
Transductive inference for text classification using support vector machines
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Abstract This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into
Support vector machines for multi-class pattern recognition.
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The solution of binary classi cation problems using support vector machines (SVMs) is well developed, but multi-class problems with more than two classes have typically been solved by combining independently produced binary classi ers. We propose a formulation of the
1-norm support vector machines
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The standard 2-norm SVM is known for its good performance in twoclass classi£ cation. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM have some advantage over the standard 2-norm SVM, especially when there are redundant noise
Support vector machines for multiple-instance learning
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This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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The output of a classifier should be a calibrated posterior probability to enable post- processing. Ëtandard ËVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized
Controlling the sensitivity of support vector machines
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For many applications it is important to accurately distinguish false negative results from false positives. This is particularly important for medical diagnosis where the correct balance between sensitivity and speci city plays an important role in evaluating the performance of a
Support vector machines for pattern classification
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Since the introduction of support vector machines , we have witnessed the huge development in theory, models, and applications of what is so-called kernel-based methods: advancement in generalization theory, kernel classifiers and regressors and their variants
Feature selection via concave minimization and support vector machines .
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Computational comparison is made between two feature selection approaches for finding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few of the n features (dimensions) as possible. In the concave
Hidden markov support vector machines
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This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which we call Hidden Markov Support Vector Machine. The
Statistical dependency analysis with support vector machines
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In this paper, we propose a method for analyzing word-word dependencies using deterministic bottom-up manner using Support Vector machines . We experimented with dependency trees converted from Penn treebank data, and achieved over 90% accuracy of
Improving the accuracy and speed of support vector machines
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Support Vector Learning Machines (SVM) are finding application in pattern recognition, regression estimation, and operator
Support vector machines : Training and applications
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Abstract The Support Vector Machine (SVM) is a new and very promising classification and function approximation technique developed by Vapnik and his group at ATT Bell Labs , and can be seen as an approximate implementation of the Structural Risk
Model selection for support vector machines
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New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the span of support vectors and rescaling of the feature space. It is shown that using these functionals, one can both predict the best choice of parameters of the
Parallel support vector machines : The cascade svm
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We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into
Support vector machines applied to face recognition
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Face recognition is a K class problem. where K is the number of known individuals; and support vector machines (SVMs) are a binary classification method. By reformulating the face recognition problem and reinterpreting the output of the SVM classifier. we developed a