Chương này trình bày một cách tiếp cận có hệ thống để thiết kế một phân loại nhị phân bằng cách sử dụng Hỗ trợ Vector Machines (SVMs). Để minh họa hiệu quả của phương pháp được đề xuất, thực nghiệm | References 239 56 Weng J. Hwang W. S. Zhang Y. and Evans C. H. Developmental Robots Theory Method and Experimental Results Proceedings of 2nd International Symposium on Humanoid Robots Tokyo Japan pp. 57-64 1999. 57 Xie M. Kandhasamy J. S. and Chia H. F. Robot Intelligence Towards machines that understand meanings International Symposium of Santa Caterina on Challenges in the Internet and Interdisciplinary Research Amalfi Coast Italy January 2004. 13 Empirical Study on Appearance-based Binary Age Classification Mohammed Yeasin Department of Computer Science State University of New York Institute of Technology Utica NY-13504 USA Rahul Khare Rajeev Sharma Computer Science and Engineering Department Pennsylvania State University University Park PA-16802 USA This chapter presents a systematic approach to designing a binary classifier using Support Vector Machines SVMs . To exemplify the efficacy of the proposed approach empirical studies were conducted in designing a classifier to classify people into different age groups using only appearance information from human facial images. Experiments were conducted to understand the effects of various issues that can potentially influence the performance of such a classifier. Linear data projection techniques such as Principal Component Analysis PCA Robust PCA RPCA and Non-Negative Matrix Factorization NMF were tested to find the best representation of the image data for designing the classifier. SVMs were used to learn the underlying model using the features extracted from the examples. Empirical studies were conducted to understand the influence of various factors such as preprocessing image resolution pose variation and gender on the classification of age group. The performances of the classifiers were also characterized in the presence of local feature occlusion and brightness gradients across the images. A number of experiments were conducted on a large data set to show the efficacy of the proposed approach. .
