Back to Results
First PageMeta Content
Mathematics / Automata theory / Algorithm / Markov chain / Computational learning theory / Symbol / Kullback–Leibler divergence / Statistics / Theoretical computer science / Applied mathematics


Learnability of Probabilistic Automata via Oracles Omri Guttman ? , S.V.N. Vishwanathan, and Robert C. Williamson Statistical Machine Learning Program National ICT Australia RSISE, Australian National University
Add to Reading List

Document Date: 2005-05-22 19:18:42


Open Document

File Size: 228,05 KB

Share Result on Facebook

City

Washington / DC / Reading / New York / /

Company

John Wiley and Sons / Oracle / ACM Press / /

Country

United States / Australia / /

/

Facility

Santa Fe Institute / Australian National University / /

IndustryTerm

state merging algorithm / property testing algorithm / modified state merging algorithm / state merging algorithms / construction algorithm / property testing algorithms / learning algorithm / /

Organization

Machine Intelligence / Australian Government’s Department of Communications / Information Technology and the Arts / Australian National University Canberra / Santa Fe Institute / Australian Research Council / IEEE Computer Society / ICT Center of Excellence / /

Person

Dana Ron / Alexander Clark / Franck Thollard / Kevin Murphy / Naftali Tishby / Michael J. Kearns / Alex Smola / Yoram Singer / /

ProgrammingLanguage

DC / /

ProvinceOrState

New York / Georgia / Massachusetts / /

PublishedMedium

the Theory of Computing / Journal of Machine Learning Research / /

Technology

modified state merging algorithm / learning algorithm / cryptography / speech recognition / state merging algorithm / property testing algorithms / Information Technology / property testing algorithm / existing L2 property testing algorithm / state merging algorithms / Sublinear algorithms / Machine Learning / construction algorithm / pdf / be efficiently learned using the state merging algorithm / /

URL

http /

SocialTag