A Algorithm to Incremental Learning with Support Vector Machine and Its Application in Multi-class Classification

2006 ◽  
Author(s):  
Zhao Ying ◽  
Wan Fuyong
Keyword(s):  
Support Vector Machine ◽  
Incremental Learning ◽  
Support Vector ◽  
A Algorithm ◽  
Multi Class Classification

A visual terrain classification method for mobile robots’ navigation based on convolutional neural network and support vector machine

2021 ◽  
pp. 014233122098791
Author(s):  
Wanli Wang ◽  
Botao Zhang ◽  
Kaiqi Wu ◽  
Sergey A Chepinskiy ◽  
Anton A Zhilenkov ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Mobile Robots ◽  
Convolutional Neural Network ◽  
Hybrid Method ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Classification Accuracy ◽  
Enhance Efficiency ◽  
Multi Class Classification

In this paper, a hybrid method based on deep learning is proposed to visually classify terrains encountered by mobile robots. Considering the limited computing resource on mobile robots and the requirement for high classification accuracy, the proposed hybrid method combines a convolutional neural network with a support vector machine to keep a high classification accuracy while improve work efficiency. The key idea is that the convolutional neural network is used to finish a multi-class classification and simultaneously the support vector machine is used to make a two-class classification. The two-class classification performed by the support vector machine is aimed at one kind of terrain that users are mostly concerned with. Results of the two classifications will be consolidated to get the final classification result. The convolutional neural network used in this method is modified for the on-board usage of mobile robots. In order to enhance efficiency, the convolutional neural network has a simple architecture. The convolutional neural network and the support vector machine are trained and tested by using RGB images of six kinds of common terrains. Experimental results demonstrate that this method can help robots classify terrains accurately and efficiently. Therefore, the proposed method has a significant potential for being applied to the on-board usage of mobile robots.

Support vector machine incremental learning triggered by wrongly predicted samples

2018 ◽  
Vol 14 (3) ◽  
pp. 232-235
Author(s):  
Ting-long Tang ◽  
Qiu Guan ◽  
Yi-rong Wu
Keyword(s):  
Support Vector Machine ◽  
Incremental Learning ◽  
Support Vector

Incremental Regressive Learning Algorithm of Support Vector Machine and its Application

2011 ◽  
Vol 216 ◽  
pp. 301-306
Author(s):  
Shi Hua Zhang ◽  
Xi Long Qu ◽  
Xue Ye Wang
Keyword(s):  
Support Vector Machine ◽  
Transition Temperature ◽  
Incremental Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Learning Ability ◽  
Support Vector ◽  
Learning Method ◽  
Essential Characteristic ◽  
Incremental Learning Method

There is no incremental learning ability for the traditional support vector machine (SVM) and there are all kind of merits and flaws for usually used incremental learning method. Normal SVM is unable to train in large-scale samples, while the computer’s memory is limited. In order to resolve this problem and improve training speed of the SVM, we analyze essential characteristic of SVM and bring up the incremental learning algorithm of SVM based on regression of SVM related to SV (support vectors). The algorithm increases the speed of training and can be able to learning with large-scale samples while its regressive precision loses fewer. The experiments show that SVM performs effectively and practically. Its application to prediction of the transition temperature (Tg) for high molecular polymers show that this model (R2=0.9427) proved to be considerably more accurate compared to a ANNs regression model (R2=0.9269).

Capsule Defects Classification Based on Hierarchical Support Vector Machines

2014 ◽  
Vol 926-930 ◽  
pp. 3373-3378 ◽  
Author(s):  
Dan Yang Qi ◽  
Zheng Jiang
Keyword(s):  
Significant Degree ◽  
Training Data ◽  
Support Vector ◽  
A Algorithm ◽  
Vector Machines ◽  
Sample Data ◽  
Feature Based ◽  
Error Accumulation ◽  
Multi Class Classification ◽  
Defects Classification

Aiming at the problem of capsule defect species diversity and classification difficulty in the process of actual capsule defect detection, this paper extracts capsule defect feature based on capsule texture, shape and capsule defect region by edge detector, and then applies hierarchical SVMs multi-class classification to classifying. In order to resolve the problems of training data imbalance and the hierarchical SVM error accumulation, a algorithm of constructing hierarchical structure is proposed that takes the principle of dividing all sample data into two more imbalanced categories according to the length of training data, and then considering significant degree of capsule defect and the probability level of capsule defect occurrence. The experimental results show that compared with the method of BP neural network, the hierarchical SVMs achieved a better classification result.

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