Method for analyzing and training linear classifier based on torque

A linear classifier and torque technology, applied in the classification field of linear classifier learning, can solve the problems of false alarm rate and unsatisfactory performance of the algorithm.

Inactive Publication Date: 2008-07-23
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

However, since the optimization objective of the maximum rejection classifier only considers the second-order mome

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  • Method for analyzing and training linear classifier based on torque
  • Method for analyzing and training linear classifier based on torque
  • Method for analyzing and training linear classifier based on torque

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Embodiment Construction

[0052]The present invention will be described in detail below in conjunction with the accompanying drawings. It should be noted that the described embodiments are only intended to facilitate the understanding of the present invention, and have no limiting effect on it.

[0053] Specific embodiments of the method introduced in the present invention will be described in more detail through the description of the above drawings.

[0054] The present invention trains an optimized linear classifier through multiple iterations, such as figure 1 As shown, the training method of the present invention includes three components: an initialization algorithm, an iterative algorithm and a termination iterative algorithm. according to figure 1 The algorithm flow shown, each step is described in detail below. Here, we assume that the training set of positive samples is X={X i} i＝1，2，...M , the training set of negative samples is Y={y i} i＝1，2，...N , where M and N are the number of posi...

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Abstract

The invention discloses a method for training linear classifier based on torque analysis, which uses an initialization method to generate a hyperplane in data space, processes iterate training on the initialized linear classifier based on torque analysis, stops the training according to iterate ending conditions to obtain one linear classifier with local optimization. The invention uses torque analysis to build linear classifier with wide application in pattern recognition, which leads physical force and torque concepts into machine learning, to train one linear classifier with local optimization. The classifier trained by the inventive method can reach local minimum correct reject rate. The invention has wide application of computer vision fields as face detection and vehicle detection, with significant theory and practical values.

Description

technical field [0001] The invention belongs to the field of pattern recognition and relates to a classification method for linear classifier learning. Background technique [0002] In the past decade, driven by the fields of artificial intelligence, robotics, and human-computer interaction, machine learning methods have attracted the attention of many research institutions. The machine learning method is to obtain the distribution law of the samples through learning on the given training sample set, and the learned rules can be used to classify unknown samples or to extract the characteristics of samples. Since machine learning algorithms do not depend on prior knowledge of relevant application domains, they have become the main research direction of pattern classification. [0003] Machine learning methods can be divided into unsupervised learning methods and supervised learning methods. Among them, the former refers to the learning algorithm without giving the category ...

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Application Information

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IPC IPC(8): G06F15/18G06N5/00G06K9/00
Inventor 田捷何晓光杨鑫
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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