Automatically assigned DDC number: 006312

Manually assigned DDC number: 006312

Number of references: 16

Title: On the Generalisation of Soft Margin Algorithms

Author:

Author:

Subject: John Shawe-taylor,Nello Cristianini On the Generalisation of Soft Margin Algorithms

Description: Generalisation bounds depending on the margin of a classifier are a relatively recent development. They provide an explanation of the performance of state-of-the-art learning systems such as Support Vector Machines (SVM) and Adaboost. The difficulty with these bounds has been either their dependence on the minimal margin or their agnostic form. The paper presents a technique for correcting those points that fail to meet a target margin, hence creating a large margin classifier at the expense of additional functional complexity. Analysis of this technique leads to bounds that motivate the previously heuristic soft margin SVM algorithms as well as justifying the use of the quadratic loss in neural network training algorithms. The results are extended to give bounds for the probability of failing to achieve a target accuracy in regression prediction. Keywords Margin, Generalisation, Soft margin, pac learning, statistical learning, support vector machines, ridge regression, ne...

Contributor: The Pennsylvania State University CiteSeer Archives

Publisher: unknown

Date: 2000-07-07

Pubyear: 2000

Format: ps

Identifier: http://citeseer.ist.psu.edu/394468.html

Source: http://www.neurocolt.org/abs/2000/../../tech_reps/2000/00082.ps.gz

Language: en

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