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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 12 of 12 1 From sBoW to dCoT: Marginalized Encoders for Text Representation Zhixiang (eddie Xu; Minmin Chen; Fei Sha... In: http://www.cse.wustl.edu/~mchen/papers/msda_dm.pdf (2012) BASE Show details 2 From sBoW to dCoT: Marginalized Encoders for Text Representation Zhixiang (eddie Xu; Minmin Chen; Fei Sha... In: http://www.cse.wustl.edu/~mchen/papers/dCoT.pdf (2012) BASE Show details 3 Large margin training of continuous density hidden Markov models Fei Sha; Lawrence K. Saul In: http://www.cs.ucsd.edu/~saul/papers/lmb08_cdhmm.pdf (2009) BASE Show details 4 Comparison of large margin training to other discriminative methods for phonetic recognition by hidden Markov models Fei Sha In: http://www.cs.berkeley.edu/~feisha/pubs/icassp2007.pdf (2007) BASE Show details 5 Comparison of large margin training to other discriminative methods for phonetic recognition by hidden markov models Fei Sha; Lawrence K. Saul In: http://www.cs.ucsd.edu/~saul/papers/icassp07_margin.pdf (2007) BASE Show details 6 Large margin hidden Markov models for automatic speech recognition Fei Sha; Lawrence K. Saul In: http://books.nips.cc/papers/files/nips19/NIPS2006_0143.pdf (2007) Abstract: We study the problem of parameter estimation in continuous density hidden Markov models (CD-HMMs) for automatic speech recognition (ASR). As in support vector machines, we propose a learning algorithm based on the goal of margin maximization. Unlike earlier work on max-margin Markov networks, our approach is specifically geared to the modeling of real-valued observations (such as acoustic feature vectors) using Gaussian mixture models. Unlike previous discriminative frameworks for ASR, such as maximum mutual information and minimum classification error, our framework leads to a convex optimization, without any spurious local minima. The objective function for large margin training of CD-HMMs is defined over a parameter space of positive semidefinite matrices. Its optimization can be performed efficiently with simple gradient-based methods that scale well to large problems. We obtain competitive results for phonetic recognition on the TIMIT speech corpus. 1 URL: http://books.nips.cc/papers/files/nips19/NIPS2006_0143.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.67.8396 BASE Hide details 7 Large margin hidden Markov models for automatic speech recognition Fei Sha; Lawrence K. Saul In: http://www.cs.berkeley.edu/~feisha/pubs/nips2006.pdf (2007) BASE Show details 8 Shallow Parsing with Conditional Random Fields Fei Sha; Fernando Pereira In: http://acl.ldc.upenn.edu/N/N03/N03-1028.pdf (2003) BASE Show details 9 Shallow parsing with conditional random fields Fei Sha; O Pereira In: http://www.cis.upenn.edu/~pereira/papers/shallow.pdf (2003) BASE Show details 10 Shallow Parsing with Conditional Random Fields Fei Sha; Fernando Pereira In: http://www.cis.upenn.edu/~feisha/pubs/shallow03.pdf (2003) BASE Show details 11 Diverse Sequential Subset Selection for Supervised Video Summarization Boqing Gong; Kristen Grauman; Fei Sha In: http://www-scf.usc.edu/%7Eboqinggo/Paper/nips14_seqdpp.pdf BASE Show details 12 Supplementary Material Geodesic Flow Kernel for Unsupervised Domain Adaptation Boqing Gong; Yuan Shi; Fei Sha... In: http://www.cs.utexas.edu/~grauman/papers/subspace-supp-cvpr2012.pdf BASE Show details

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