Adaptive learning of multi-finger motion recognition based on support vector machine

2013 ◽  
Author(s):  
Dapeng Yang ◽  
Li Jiang ◽  
Rongqiang Liu ◽  
Hong Liu
Keyword(s):  
Support Vector Machine ◽  
Adaptive Learning ◽  
Support Vector ◽  
Motion Recognition ◽  
Finger Motion

scholarly journals Iterative Reweighted Noninteger Norm Regularizing SVM for Gene Expression Data Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jianwei Liu ◽  
Shuang Cheng Li ◽  
Xionglin Luo
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Adaptive Learning ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Training Dataset ◽  
Support Vector ◽  
Data Set ◽  
Cancer Data ◽  
Public Data

Support vector machine is an effective classification and regression method that uses machine learning theory to maximize the predictive accuracy while avoiding overfitting of data.L2regularization has been commonly used. If the training dataset contains many noise variables,L1regularization SVM will provide a better performance. However, bothL1andL2are not the optimal regularization method when handing a large number of redundant values and only a small amount of data points is useful for machine learning. We have therefore proposed an adaptive learning algorithm using the iterative reweightedp-norm regularization support vector machine for 0 <p≤ 2. A simulated data set was created to evaluate the algorithm. It was shown that apvalue of 0.8 was able to produce better feature selection rate with high accuracy. Four cancer data sets from public data banks were used also for the evaluation. All four evaluations show that the new adaptive algorithm was able to achieve the optimal prediction error using apvalue less thanL1norm. Moreover, we observe that the proposedLppenalty is more robust to noise variables than theL1andL2penalties.

scholarly journals Efficient electro encephelogram classification system using support vector machine classifier and adaptive learning technique

2022 ◽  
Vol 25 (1) ◽  
pp. 291
Author(s):  
Virupaxi Balachandra Dalal ◽  
Satish S. Bhairannawar
Keyword(s):  
Support Vector Machine ◽  
Adaptive Learning ◽  
Classification System ◽  
Training Data ◽  
Support Vector ◽  
Detection Accuracy ◽  
Eeg Signals ◽  
The Neural Network ◽  
Eeg Data ◽  
Learning Technique

Complex <span>modern signal processing is used to automate the analysis of electro encephelogram (EEG) signals. For the diagnosis of seizures, approaches that are simple and precise may be preferable rather than difficult and time-consuming. In this paper, efficient EEG classification system using support vector machine (SVM) and Adaptive learning technique is proposed. The database EEG signals are subjected to temporal and spatial filtering to remove unwanted noise and to increase the detection accuracy of the classifier by selecting the specific bands in which most of the EEG data are present. The neural network based SVM is used to classify the test EEG data with respect to training data. The cost-sensitive SVM with proposed Adaptive learning classifies the EEG signals where the adaptive learning with probability based function helps in prediction of the future samples and this leads in improving the accuracy with detection time. The detection accuracy of the proposed algorithm is compared with existing which shows that the proposed algorithm can classify the EEG signal more </span>effectively.

A Novel Analysis of Advanced Visual Cryptography Techniques for Providing Security Against Web Attacks Using Support Vector Machine Technique

2020 ◽  
Vol 17 (5) ◽  
pp. 2097-2114
Author(s):  
Venkata Satya Vivek Tammineedi ◽  
V.N. Rajavarman
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Data Storage ◽  
Adaptive Learning ◽  
Web Application ◽  
Visual Cryptography ◽  
Support Vector ◽  
Internet Applications ◽  
Authentication Mechanism ◽  
Visual Security

In today’s internet applications such as some real time application services like core banking and other public service oriented application have been major issue in authentication of user specification. To perform online dictionary attacks, passwords have been used for security and authentication mechanism. Present days, hacking of databases on web oriented applications is unavoidable to access them easily. Data maintenance is a complex task in internet applications. To solve these type of problems in internet applications, in this paper, we proposed a novel Integrated and Dynamic CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) (I&D CAPTCHA), which is extension version of existing CAPTCHA that valuated third party human attacks in internet applications based Visual Cryptography approach to discuss about authentication problem in real time applications. There is more number of methods presented for security in advanced pictures for insurance from inventive uninvolved or dynamic assaults in system correspondence environment. Like insightful Visual Cryptographic (VC) is a cutting edge strategy, which is utilized to mystery picture safely impart furthermore keep up to privacy. To proceed with difficulties of security in advanced picture information sharing, so in this paper we break down various VC security instruments for computerized picture information offering to regard tomystery information secrecy. Our examination give effective security answers for relative mystery advanced picture information imparting to correspondence progressively environment. Security aspects are main concepts in present days because of increasing statistical data storage. In Artificial Intelligence (AI) oriented applications, it is very difficult in terms of protection to increasing new aspects in real time world. So we also plan a Novel and Advanced Security system to enable solution for basic AI problems in this paper. This framework mainly works based on Captcha as visual security passwords (CaRP); it is two way communication plan which means that, it is the combination of Captcha and visual security plan. Our approach mainly worked with image security with respect to selection of passwords based on random way. In this paper, we also propose AMODS, an adaptive system that periodically updates the detection model to detect the latest unknown attacks. We also propose an adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. Our system out performs existing web attack detectionmethods, with an F-value of 94.79% and FP rate of 0.09%. The total number of malicious queries obtained by SVM HYBRID is 2.78 times that by the popular Support Vector Machine Adaptive Learning (SVMAL) method. The malicious queries obtained can be used to update the Web Application Firewall (WAF) signature library.

scholarly journals Method of Analyzing and Managing Volleyball Action by Using Action Sensor of Mobile Device

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xu Sun ◽  
Kai Zhao ◽  
Wei Jiang ◽  
Xinlong Jin
Keyword(s):  
Pattern Recognition ◽  
Support Vector Machine ◽  
Feature Extraction ◽  
Human Motion ◽  
Classification Algorithm ◽  
Support Vector ◽  
Motion Sensors ◽  
Support Vector Machine Algorithm ◽  
Motion Information ◽  
Motion Recognition

With the development of electronic technology and sensor technology, more and more intelligent electronic devices integrate micro inertial sensors, which makes the research of human action recognition based on action sensing data have great application value. Data-based action recognition is a new research direction in the field of pattern recognition, which is essentially a process of action data acquisition, feature extraction, feature extraction, and recognition, the process of classification and recognition. Inertial motion information includes acceleration and angular velocity information, which is ubiquitous in daily life. Compared with motion recognition based on visual information, it can more directly reflect the meaning of action. This study mainly discusses the method of analyzing and managing volleyball action by using the action sensor of mobile device. Based on the motion recognition algorithm of support vector machine, the motion recognition process of support vector machine is constructed. When the data terminal and gateway of volleyball players are not in the same LAN, the classification algorithm classifies the samples to be tested through the characteristic data, which directly affects the recognition results. In this paper, the support vector machine algorithm is selected as the data classification algorithm, and the calculation of the classification process is reduced by designing an appropriate kernel function. For multiclass problems, the hierarchical structure of directed acyclic graph is optimized to improve the recognition rate. We need to bind motion sensors to human joints. In order to realize real-time recognition of human motion, mobile devices need to add windows to the motion capture data, that is, divide the data into a small sequence of specified length, and provide more application scenarios for the device. This method of embedding motion sensors into devices to read motion information is widely used, which provides a convenient data acquisition method for human motion pattern recognition based on motion information. The multiclassification support vector machine algorithm is used to train the classification algorithm model with action data. When the signal strength of the sensor is 90 t and the speed is 2.0 m/s and 0.5 m/s, the detection accuracy of the adaptive threshold is 93% and 95%, respectively. The results show that the SVM method based on hybrid kernel function can greatly improve the recognition accuracy of volleyball stroke, and the recognition time is short.

A novel adaptive support vector machine method for reliability analysis

2021 ◽  
pp. 1748006X2110033
Author(s):  
Zhaoyin Shi ◽  
Zhenzhou Lu ◽  
Xiaobo Zhang ◽  
Luyi Li
Keyword(s):  
Support Vector Machine ◽  
Reliability Analysis ◽  
Failure Probability ◽  
Adaptive Learning ◽  
Limit State ◽  
Support Vector ◽  
State Function ◽  
Limit State Function ◽  
Learning Function ◽  
Svm Model

For the structural reliability analysis, although many methods have been proposed, they still suffer from substantial computational cost or slow convergence rate for complex structures, the limit state function of which are highly non-linear, high dimensional, or implicit. A novel adaptive surrogate model method is proposed by combining support vector machine (SVM) and Monte Carlo simulation (MCS) to improve the computational efficiency of estimating structural failure probability in this paper. In the proposed method, a new adaptive learning method is established based on the kernel function of the SVM, and a new stop criterion is constructed by measuring the relative position between sample points and the margin of SVM. Then, MCS is employed to estimate failure probability based on the convergent SVM model instead of the actual limit state function. Due to the introduction of adaptive learning function, the effectiveness of the proposed method is significantly higher than those that employed random training set to construct the SVM model only once. Compared with the existing adaptive SVM combined with MCS, the proposed method avoids information loss caused by inconsistent distance scales and the normalization of the learning function, and the proposed convergence criterion is also more concise than that employed in the existing method. The examples in the paper show that the proposed method is more efficient and has broader applicability than other similar surrogate methods.

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