'Gabor features' presentation slideshows

9.0 Speech Recognition Updates

9.0 Speech Recognition Updates. Minimum-Classification-Error (MCE) and Discriminative Training. A Primary Problem with the Conventional Training Criterion : Confusing sets find ? ( i ) such that P(X| ? ( i ) ) is maximum (Maximum Likelihood) if X ? C i

895 views • 89 slides

9.0 Speech Recognition Updates

9.0 Speech Recognition Updates. Minimum-Classification-Error (MCE) and Discriminative Training. A Primary Problem with the Conventional Training Criterion : Confusing sets find ? ( i ) such that P(X| ? ( i ) ) is maximum (Maximum Likelihood) if X ? C i

894 views • 89 slides

9.0 Speech Recognition Updates

9.0 Speech Recognition Updates. Minimum-Classification-Error (MCE) and Discriminative Training. A Primary Problem with the Conventional Training Criterion : Confusing sets find ? ( i ) such that P(X| ? ( i ) ) is maximum (Maximum Likelihood) if X ? C i

942 views • 76 slides

9.0 Speech Recognition Updates

9.0 Speech Recognition Updates. Minimum-Classification-Error (MCE) and Discriminative Training. A Primary Problem with the Conventional Training Criterion : Confusing sets find ? ( i ) such that P(X| ? ( i ) ) is maximum (Maximum Likelihood) if X ? C i

927 views • 75 slides

Visual Features for Content-based Medical Image Retrieval

287 views • 16 slides

Hierarchical Ensemble of Global and Local Classifiers for Face Recognition

Hierarchical Ensemble of Global and Local Classifiers for Face Recognition. Yu Su, Shiguang Shan , Member, IEEE , Xilin Chen , Member, IEEE , and Wen Gao , Fellow, IEEE. I. INTRODUCTION.

248 views • 14 slides

Adam Yeh

Adam Yeh. UCF Computer Vision REU Week 4. Coding Problems. Problem: Adaboost taking too much memory: 160k features, feature value as int (4 bytes) Maximum heap size: 1.5GB Each picture takes 160k*4=640kB ~2000 pictures can be trained on Solution: store less information

164 views • 8 slides