Support-Vector Networks
Corinna Cortes,Vladimir Vapnik +1 more
TLDR
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.Abstract:
The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to a very high-dimension feature space. In this feature space a linear decision surface is constructed. Special properties of the decision surface ensures high generalization ability of the learning machine. The idea behind the support-vector network was previously implemented for the restricted case where the training data can be separated without errors. We here extend this result to non-separable training data.
High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated. We also compare the performance of the support-vector network to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.read more
Citations
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Proceedings ArticleDOI
Dependency Tree Kernels for Relation Extraction
Aron Culotta,Jeffrey Sorensen +1 more
TL;DR: This work extends previous work on tree kernels to estimate the similarity between the dependency trees of sentences, and uses this kernel within a Support Vector Machine to detect and classify relations between entities in the Automatic Content Extraction (ACE) corpus of news articles.
Journal ArticleDOI
Support vector machine approach for protein subcellular localization prediction.
Sujun Hua,Zhirong Sun +1 more
TL;DR: Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions and can be a complementary method to other existing methods based on sorting signals.
Journal ArticleDOI
An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels
TL;DR: An insight into ELMs in three aspects, viz: random neurons, random features and kernels is provided and it is shown that in theory ELMs (with the same kernels) tend to outperform support vector machine and its variants in both regression and classification applications with much easier implementation.
Journal ArticleDOI
A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
TL;DR: In this paper, three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were compared for landslide susceptibility mapping at Penang Hill area, Malaysia.
Journal ArticleDOI
Support vector machines for classification in remote sensing
Mahesh Pal,Paul M. Mather +1 more
TL;DR: Results show that the SVM achieves a higher level of classification accuracy than either the ML or the ANN classifier, and that theSVM can be used with small training datasets and high‐dimensional data.
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