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Minimum Phoneme Error Based Heteroscedastic Linear Discriminant Analysis for Speech Recognition

Minimum Phoneme Error Based Heteroscedastic Linear Discriminant Analysis for Speech Recognition

Minimum Phoneme Error Based Heteroscedastic Linear Discriminant Analysis for Speech Recognition. Bing Zhang and Spyros Matsoukas BBN Technologies. Present by shih-hung Liu 2006/ 05/16. Outline. Review PCA, LDA, HLDA Introduction MPE-HLDA Experimental results Conclusions. Review - PCA.

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342 views • 22 slides
Get Rich and Cure Cancer with Support Vector Machines

Get Rich and Cure Cancer with Support Vector Machines

Get Rich and Cure Cancer with Support Vector Machines. (Your Summer Projects). Kernel Trick. https://www.youtube.com/watch?v= 3liCbRZPrZA. This is achieved with a polynomial kernel. Feature map: Kernel:. Optimization of transformed problem: Only kernel matters.

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352 views • 20 slides
Recognition of Fragmented Characters Using Multiple Feature-Subset Classifiers

Recognition of Fragmented Characters Using Multiple Feature-Subset Classifiers

Recognition of Fragmented Characters Using Multiple Feature-Subset Classifiers. Institute of Information Science, Academia Sinica, Taiwan C.H. Chou, C.Y. Guo, and F. Chang. Inter. Conf. Document Analysis and Recognition 2007. Introduction.

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313 views • 19 slides
Context-Aware Clustering

Context-Aware Clustering

Context-Aware Clustering. Junsong Yuan and Ying Wu EECS Dept., Northwestern University. Contextual pattern and co-occurrences. ?. Spatial contexts provide useful cues for clustering . K-means revisit. EM Update. Binary label indicator. Assumption: data samples are independent.

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282 views • 17 slides

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