bookmarks (hide)3
- Causal Reasoning, PGMs
- The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models. HTK is primarily used for speech recognition research although it has been used for numerous other applications including research into speech synthesis, character recognition and DNA sequencing. HTK is in use at hundreds of sites worldwide. HTK consists of a set of library modules and tools available in C source form. The tools provide sophisticated facilities for speech analysis, HMM training, testing and results analysis. The software supports HMMs using both continuous density mixture Gaussians and discrete distributions and can be used to build complex HMM systems. The HTK release contains extensive documentation and examples.
- This page is devoted to learning methods building on kernels, such as the support vector machine. It grew out of earlier pages at the Max Planck Institute for Biological Cybernetics and at GMD FIRST, snapshots of which can be found here and here. In those days, information about kernel methods was sparse and nontrivial to find, and the kernel machines web site acted as a central repository for the field. It included a list of people working in the field, and online preprints of most publications. Nowadays, this no longer makes sense, partly because the field is very popular, so there are too many people and papers to make such lists useful, and partly because search engines do the job much more conveniently. But what really forced us to do a major update of the site was the fact that spammers discovered our site, and it was no longer possible to operate a system which was built on the trust that people who submit an entry do so to improve the quality of the site.
publications (hide)38
- PODC, page 385-394. ACM, (2015)
- Very Short Introductions Oxford University Press, Oxford, UK, (2018)
- Information Processing & Management, 43 (1): 169--180 (January 2007)
- Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, page 1419--1420. New York, NY, ACM, (2017)
- Synthesis Lectures on Information Concepts, Retrieval, and Services, 1 (1): 1 -- 111 (2009)
- Higher Education, 57 (3): 373--391 (Mar 1, 2009)
- The Leadership Quarterly, 27 (2): 200--217 (April 2016)
- Cambridge University Press, New York, NY, (2001)
- Swansea University, (2013)
- Language, 89 (1): 4--29 (March 2013)
- Adv Child Dev Behav., (2014)
- Interdisciplinary Journal of Problem-Based Learning, (May 2006)
- Social Science Research, (May 2017)
- Journal of Engineering Education, 103 (4): 599--624 (2014)
- IEEE Transactions on Big Data, (2017)
- TechTrends, 60 (6): 565--568 (2016)
- Pearson Education, Upper Saddle River, NJ, Third edition, (2009)
- Bloomsbury Sigma, (2017)
- Pearson, Boston, Massachusetts, (2006)
- Wadsworth Publishing, Fourth edition, (1992)
