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163
A practical guide to support vector classification
, 2010
"... The support vector machine (SVM) is a popular classification technique. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure which usually gives reasonable results. ..."
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Cited by 787 (7 self)
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The support vector machine (SVM) is a popular classification technique. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure which usually gives reasonable results.
Combining svms with various feature selection strategies
- Taiwan University
, 2005
"... Feature selection is an important issue in many research areas. There are some reasons for selecting important features such as reducing the learning time, improving the accuracy, etc. This thesis investigates the performance of combining support vector machines (SVM) and various feature selection s ..."
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Cited by 126 (0 self)
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Feature selection is an important issue in many research areas. There are some reasons for selecting important features such as reducing the learning time, improving the accuracy, etc. This thesis investigates the performance of combining support vector machines (SVM) and various feature selection strategies. The first part of the thesis mainly describes the existing feature selection methods and our experience on using those methods to attend a competition. The second part studies more feature selection strategies using the SVM. ii �ì��¬¡÷ � ��å�ç¢�ß��� � selection)��¥ì����£��È�� ����È������Ú���£����æÁ ç��£�����û�� ì�Öù�¡�È��(feature é£�æÁ©Â����℄���� � �Ü � ����Æ���È��℄�¡��û���℄�ø�¢�§���� �(Support Vector Machine) iii
Large scale transductive svms
- JMLR
"... We show how the Concave-Convex Procedure can be applied to Transductive SVMs, which traditionally require solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach. Software is a ..."
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Cited by 92 (5 self)
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We show how the Concave-Convex Procedure can be applied to Transductive SVMs, which traditionally require solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach. Software is available at
A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods
, 2003
"... The sigmoid kernel was quite popular for support vector machines due to its origin from neural networks. However, as the kernel matrix may not be positive semidefinite (PSD), it is not widely used and the behavior is unknown. In this paper, we analyze such non-PSD kernels through the point of view o ..."
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Cited by 91 (5 self)
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The sigmoid kernel was quite popular for support vector machines due to its origin from neural networks. However, as the kernel matrix may not be positive semidefinite (PSD), it is not widely used and the behavior is unknown. In this paper, we analyze such non-PSD kernels through the point of view of separability. Based on the investigation of parameters in different ranges, we show that for some parameters, the kernel matrix is conditionally positive definite (CPD), a property which explains its practical viability. Experiments are given to illustrate our analysis. Finally, we discuss how to solve the non-convex dual problems by SMO-type decomposition methods. Suitable modifications for any symmetric non-PSD kernel matrices are proposed with convergence proofs.
A nonparametric approach to bottom-up visual saliency
- In NIPS
, 2006
"... This paper addresses the bottom-up influence of local image information on hu-man eye movements. Most existing computational models use a set of biolog-ically plausible linear filters, e.g., Gabor or Difference-of-Gaussians filters as a front-end, the outputs of which are nonlinearly combined into a ..."
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Cited by 61 (3 self)
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This paper addresses the bottom-up influence of local image information on hu-man eye movements. Most existing computational models use a set of biolog-ically plausible linear filters, e.g., Gabor or Difference-of-Gaussians filters as a front-end, the outputs of which are nonlinearly combined into a real number that indicates visual saliency. Unfortunately, this requires many design parameters such as the number, type, and size of the front-end filters, as well as the choice of nonlinearities, weighting and normalization schemes etc., for which biological plausibility cannot always be justified. As a result, these parameters have to be chosen in a more or less ad hoc way. Here, we propose to learn a visual saliency model directly from human eye movement data. The model is rather simplistic and essentially parameter-free, and therefore contrasts recent developments in the field that usually aim at higher prediction rates at the cost of additional parameters and increasing model complexity. Experimental results show that—despite the lack of any biological prior knowledge—our model performs comparably to existing ap-proaches, and in fact learns image features that resemble findings from several pre-vious studies. In particular, its maximally excitatory stimuli have center-surround structure, similar to receptive fields in the early human visual system. 1
Robust support vector method for hyperspectral data classification and knowledge discovery
- IEEE Transactions on Geoscience and Remote Sensing
, 2004
"... Abstract — In this paper, we propose the use of Support Vector Machines (SVM) for automatic hyperspectral data classification and knowledge discovery. In the first stage of the study, we use SVMs for crop classification and analyze their performance in terms of efficiency and robustness, as compared ..."
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Cited by 36 (7 self)
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Abstract — In this paper, we propose the use of Support Vector Machines (SVM) for automatic hyperspectral data classification and knowledge discovery. In the first stage of the study, we use SVMs for crop classification and analyze their performance in terms of efficiency and robustness, as compared to extensively used neural and fuzzy methods. Efficiency is assessed by evaluating accuracy and statistical differences in several scenes. Robustness is analyzed in terms of (a) suitability to working conditions when a feature selection stage is not possible, and (b) performance when different levels of Gaussian noise are introduced at their inputs. In the second stage of this work, we analyze the distribution of the support vectors (SV) and perform sensitivity analysis on the best classifier in order to analyze the significance of the input spectral bands. For classification purposes, six hyperspectral images acquired with the 128-band HyMAP spectrometer during the DAISEX-1999 campaign are used. Six crop classes were labelled for each image. A reduced set of labelled samples is used to train the models and the entire images are used to assess their performance. Several conclusions are drawn: (1) SVMs yield better outcomes than neural networks regarding accuracy, simplicity and robustness; (2) training neural and neurofuzzy models is unfeasible when working with high dimensional input spaces and great amounts of training data; (3) SVMs perform similarly for different training subsets with varying input dimension, which indicates that noisy bands are successfully detected; and (4) a valuable ranking of bands through sensitivity analysis is achieved. Index Terms — Hyperspectral imagery, crop classification, knowledge discovery, Support Vector Machines, neural networks.
Learning to identify definitions using syntactic features
- Proceedings of the EACL workshop on Learning Structured Information in Natural Language Applications
, 2006
"... This paper describes an approach to learning concept definitions which operates on fully parsed text. A subcorpus of the Dutch version of Wikipedia was searched for sentences which have the syntactic properties of definitions. Next, we experimented with various text classification techniques to dist ..."
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Cited by 33 (5 self)
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This paper describes an approach to learning concept definitions which operates on fully parsed text. A subcorpus of the Dutch version of Wikipedia was searched for sentences which have the syntactic properties of definitions. Next, we experimented with various text classification techniques to distinguish actual definitions from other sentences. A maximum entropy classifier which incorporates features referring to the position of the sentence in the document as well as various syntactic features, gives the best results. 1
Recent Advances of Large-scale Linear Classification
"... Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the performance (i.e., testing accuracy) of linear classifiers has shown to be close to that of nonlinear classifiers such as kernel methods, but training and testing speed is much ..."
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Cited by 33 (6 self)
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Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the performance (i.e., testing accuracy) of linear classifiers has shown to be close to that of nonlinear classifiers such as kernel methods, but training and testing speed is much faster. Recently, many research works have developed efficient optimization methods to construct linear classifiers and applied them to some large-scale applications. In this paper, we give a comprehensive survey on the recent development of this active research area.
A Study on SMO-type Decomposition Methods for Support Vector Machines
"... Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection rules. This article studies Sequential Minimal Optimizatio ..."
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Cited by 31 (4 self)
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Decomposition methods are currently one of the major methods for training support vector machines. They vary mainly according to different working set selections. Existing implementations and analysis usually consider some specific selection rules. This article studies Sequential Minimal Optimization (SMO)-type decomposition methods under a general and flexible way of choosing the two-element working set. Main results include: 1) a simple asymptotic convergence proof, 2) a general explanation of the shrinking and caching techniques, and 3) the linear convergence of the methods. Extensions to some SVM variants are also discussed.
A stopping criterion for active learning
, 2007
"... Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a s ..."
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Cited by 22 (0 self)
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Active learning (AL) is a framework that attempts to reduce the cost of annotating training material for statistical learning methods. While a lot of papers have been presented on applying AL to natural language processing tasks reporting impressive savings, little work has been done on defining a stopping criterion. In this work, we present a stopping criterion for active learning based on the way instances are selected during uncertainty-based sampling and verify its applicability in a variety of settings. The statistical learning models used in our study are support vector machines (SVMs), maximum entropy models and Bayesian logistic regression and the tasks performed are text classification, named entity recognition and shallow parsing. In addition, we present a method for multiclass mutually exclusive SVM active learning.
