Protein Fold Recognition Using Adaboost Learning Strategy
نویسندگان
چکیده
منابع مشابه
Boosting for Fast Face Recognition
We propose to use the AdaBoost algorithm for face recognition. AdaBoost is a kind of large margin classifiers and is efficient for on-line learning. In order to adapt the AdaBoost algorithm to fast face recognition, the original Adaboost which uses all given features is compared with the boosting along feature dimensions. The comparable results assure the use of the latter, which is faster for ...
متن کاملSoft Margins for Adaboost Produced as Part of the Esprit Working Group in Neural and Computational Learning Ii, Neurocolt2 27150
Recently ensemble methods like AdaBoost were successfully applied to character recognition tasks, seemingly defying the problems of overrtting. This paper shows that although AdaBoost rarely overrts in the low noise regime it clearly does so for higher noise levels. Central for understanding this fact is the margin distribution and we nd that AdaBoost achieves { doing gradient descent in an err...
متن کاملGabor Feature Selection for Face Recognition Using Improved AdaBoost Learning
Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual information into AdaBoost, we propose an improved boosting algorithm in this paper. The proposed method fully examines the redundancy between candidate classifiers and selected classifiers. The classifiers thus selected ar...
متن کاملPractical Aspects of Face Recognition
Current systems for face recognition techniques often use either SVM or Adaboost techniques for face detection part and use PCA for face recognition part. In this paper, we offer a novel method for not only a powerful face detection system based on Six-segment-filters (SSR) and Adaboost learning algorithms but also for a face recognition system. A new exclusive face detection algorithm has been...
متن کاملFace Verification Based on AdaBoost Learning for Histogram of Gabor Phase Patterns (HGPP) Selection and Samples Synthesis with Quotient Image Method
Face verification technology is widely used in public safety, e-commerce, access control, and so on. We propose a novel face verification approach, which combines a relatively new object descriptor—Histogram of Gabor Phase Patterns (HGPP), AdaBoost Algorithm selecting HGPP features and learning binary classifier, and Quotient Image method synthesizing face images under new illumination conditio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010