نتایج جستجو برای: libsvm

تعداد نتایج: 168  

2017
Lingyun Gao Mingquan Ye Xiaojie Lu Daobin Huang

It remains a great challenge to achieve sufficient cancer classification accuracy with the entire set of genes, due to the high dimensions, small sample size, and big noise of gene expression data. We thus proposed a hybrid gene selection method, Information Gain-Support Vector Machine (IG-SVM) in this study. IG was initially employed to filter irrelevant and redundant genes. Then, further remo...

2008
Yaakov HaCohen-Kerner Ariel Kass Ariel Peretz

Abbreviations are widely used in many languages and disambiguation of abbreviations is critical. In this research, a structured process that attempts to solve the problem of abbreviation ambiguity is presented. Various baseline methods have been explored, including context-related methods and statistical methods. Almost all methods are domain-independent and language independent. The applicatio...

2013
Mohit Rana Nalin Gupta Josue L. Dalboni Da Rocha Sangkyun Lee Ranganatha Sitaram

There is a recent increase in the use of multivariate analysis and pattern classification in prediction and real-time feedback of brain states from functional imaging signals and mapping of spatio-temporal patterns of brain activity. Here we present MANAS, a generalized software toolbox for performing online and offline classification of fMRI signals. MANAS has been developed using MATLAB, LIBS...

2016
Yang You Xiangru Lian Ji Liu Hsiang-Fu Yu Inderjit S. Dhillon James Demmel Cho-Jui Hsieh

In this paper, we propose and study an Asynchronous parallel Greedy Coordinate Descent (Asy-GCD) algorithm for minimizing a smooth function with bounded constraints. At each iteration, workers asynchronously conduct greedy coordinate descent updates on a block of variables. In the first part of the paper, we analyze the theoretical behavior of Asy-GCD and prove a linear convergence rate. In the...

2012
Stefan Edelkamp Martin Stommel

In this paper we present and evaluate a simple but effective machine learning algorithm that we call Bitvector Machine: Feature vectors are partitioned along component-wise quantiles and converted into bitvectors that are learned. It is shown that the method is efficient in both training and classification. The effectiveness of the method is analysed theoretically for best and worst-case scenar...

2012
Tom de Ruijter

In this work I address the issue of large scale learning in an online setting. To tackle it, I introduce a novel algorithm that enables semi-supervised learning in an online fashion. By combining state-of-the-art online methods such as Pegasos [3] with the multi-view co-regularization framework, I achieve significantly better performance on regression and binary classification tasks. This shows...

2004
Huayang Xie Peter Andreae Mengjie Zhang Paul Warren

This paper describes an approach to the detection of stress in spoken New Zealand English. After identifying the vowel segments of the speech signal, the approach extracts two different sets of features — prosodic features and vowel quality features — from the vowel segments. These features are then normalised and scaled to obtain speaker independent feature values that can be used to classify ...

2011
Jair Cervantes Asdrúbal López Chau Farid García Adrián Trueba

In this paper we present a new algorithm to speed up the training time of Support Vector Machines (SVM). SVM has some important properties like solid mathematical background and a better generalization capability than other machines like for example neural networks. On the other hand, the major drawback of SVM occurs in its training phase, which is computationally expensive and highly dependent...

2006
Richard Johansson Pierre Nugues

In this paper, we describe a system for the CoNLL-X shared task of multilingual dependency parsing. It uses a baseline Nivre’s parser (Nivre, 2003) that first identifies the parse actions and then labels the dependency arcs. These two steps are implemented as SVM classifiers using LIBSVM. Features take into account the static context as well as relations dynamically built during parsing. We exp...

2006
Gang Kou Yi Peng Yong Shi Zhengxin Chen

Mathematical programming based methods have been applied to credit risk analysis and have proven to be powerful tools. One challenging issue in mathematical programming is the computation complexity in finding optimal solutions. To overcome this difficulty, this paper proposes a Multi-criteria Convex Quadratic Programming model (MCCQP). Instead of looking for the global optimal solution, the pr...

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