| Author: | Cui, Zhenxi |
| Title: | Optimization of multi-attribute multi-label object recognition using support vector machine |
| Advisors: | Chu, Kar Hang ((ME) |
| Degree: | M.Sc. |
| Year: | 2016 |
| Subject: | Machine learning. Support vector machines. Human-robot interaction. Hong Kong Polytechnic University -- Dissertations |
| Department: | Faculty of Engineering |
| Pages: | v, 92 pages : illustrations |
| Language: | English |
| Abstract: | Intelligent machine learning has been a hot research topic in recent years and its technique has been utilized for a variety of tasks such as object identification, face detection, and body gesture recognition. Over the past few years, an increasing number of research has been done, aiming to improve the recognition accuracy and the response time so that the recognition process could be performed in real-time with no delay. Typical machine learning algorithms that have been considered by researchers include neural network, K-nearest neighbors (K-NN), and support vector machines (SVM). Although different machine learning algorithms have been proposed, the efficiency of machine learning algorithms for applications related to human-computer interaction (HCI), in general, is relatively low. This is mainly due to the lack of robustness insensory data interpretation techniques. To address this issue, this dissertation developed a novel numerical approach based on support vector machine(SVM). The kernel function is the core of the SVM, and choosing the parameters is very important toward the final results for the regression and classification. So this work focuses on the method in parameter optimization so as to improve the accuracy rate while reducing the response time and the computation consumption. Two different kinds of data set, namely the iris data-set, and the spammed email data-set, were used to validate the method proposed in this work. Results were analyzed and recommendations were made for future research. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b29170230.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.4 MB | Adobe PDF | View/Open |
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