Support Vector Machine (original paper) | AISC Foundational

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Toronto Deep Learning Series
https://aisc.a-i.science/events/2019-01-31/

Support Vector Machine
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



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Tags:
machine learning
support vector machine
support vector network
classification
OCR
ai
artificial intelleigence