Neural network sample selection method and device based on active learning

A neural network and active learning technology, applied in the field of machine learning, can solve problems such as high algorithm complexity and inapplicability, achieve the effect of reducing the number of training samples, reducing time and cost, and improving classification effects

Inactive Publication Date: 2012-06-13
HOHAI UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, the complexity of this algorithm is extremely high, and it is not applicable to many complex problems.

Method used

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  • Neural network sample selection method and device based on active learning
  • Neural network sample selection method and device based on active learning
  • Neural network sample selection method and device based on active learning

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Embodiment Construction

[0024] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0025] Taking the multi-layer perceptron neural network as an example, the method for selecting samples of the feedforward neural network according to the present invention is described. However, those skilled in the art should understand that the present invention is not limited to MLP neural networks, but can be applied to other feed-forward neural networks.

[0026] MLP is a fully connected feed-forward neural network suitable for object classification. The structure of the MLP is as figure 1 As shown, it is a three-layer feed-forward network: the input layer MA is composed of input pattern nodes, x i Represents the i-th component of the input pattern vector (i=1, 2, ..., n); the second layer is the hidden layer MB, which consists of m nodes b j (j=1, 2, . . . , m) composition. The third layer is the output layer MC, which consists of ...

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Abstract

The invention discloses a neural network sample selection method based on active learning, belonging to the field of machinery learning in intelligent science and technology. The neural network sample selection method comprises the following steps of: taking sensibility as a reference, and looking for sample points with severely-changed periphery through selecting the sample points with large sensibility, wherein these sample points are often important to a training classifier. The invention further discloses a neural network sample selection device based on active learning. According to the neural network sample selection method and device based on active learning, disclosed by the invention, the number of necessarily marked sample points can be effectively reduced, and the performance of a classifier is improved through reducing the marks.

Description

technical field [0001] The invention relates to a learning sample selection method and a device for neural network design, in particular to an active learning-based learning sample selection method and a device that can effectively improve neural network classification efficiency, and belongs to the field of machine learning in intelligent science and technology. Background technique [0002] When designing a neural network classifier, the labeling of training samples is generally done by experts, which often costs a lot of money and time. In the past, when selecting samples to be labeled, they were generally randomly selected from the obtained unlabeled samples. Training a classifier in this way often requires a large number of labeled samples, which requires a lot of manpower and material resources, and it is easy to prolong the training time. [0003] Active learning technology is to filter unlabeled samples through certain criteria, and then manually mark the obtained s...

Claims

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Application Information

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IPC IPC(8): G06N3/08
Inventor 储荣王敏
Owner HOHAI UNIV
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