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J–Machine
Results: 754

#	Item
631	Support Vector Machines for Noise Robust ASR M. J. F. Gales, A. Ragni, H. AlDamarki and C. Gautier Cambridge University Engineering Department Trumpington St., Cambridge CB2 1PZ, U.K. {mjfg,ar527,hia21,cg291}@eng.cam.ac. Add to Reading List Source URL: mi.eng.cam.ac.uk Language: English - Date: 2010-09-14 05:24:32 Computational linguistics Statistical classification Support vector machine Classifier Pattern recognition Hidden Markov model Speech recognition Speech synthesis Statistics Machine learning Artificial intelligence
632	Review of the Documentation of the Sequoia Voting System Aaron J. Burstein University of California, Berkeley1 Nathan S. Good Add to Reading List Source URL: www.sos.ca.gov Language: English - Date: 2011-05-14 16:39:28 Politics Certification of voting machines Voting machine Reliability engineering Sequoia Technology Systems engineering Sequoia Voting Systems Election technology Electronic voting Information society
633	Source Code Review of the Diebold Voting System Joseph A. Calandrino Ariel J. Feldman J. Alex Halderman Add to Reading List Source URL: www.sos.ca.gov Language: English - Date: 2011-05-14 16:39:41 Technology Voter-verified paper audit trail Premier Election Solutions Diebold Electoral fraud Gems Voting machine Vulnerability Computer security Electronic voting Election fraud Politics
634	Microsoft Word - ZZ J.3 Administration Report- Revised.doc Add to Reading List Source URL: www.fairelectionswi.com Language: English - Date: 2009-11-10 16:14:00 Voting Electronic voting Single winner electoral systems Optical scan voting system Voting machine Ballot Vote counting system Election Systems & Software Secret ballot Politics Election technology Elections
635	difficulty with which applications written in Java could be ported from Windows to other platforms, and vice versa. A. Creating a Java Implementation for Windows that Undermined Portability and Was Incompatible with Othe Add to Reading List Source URL: www.justice.gov Language: English - Date: 2006-04-11 10:35:14 Cross-platform software Computing platforms Java programming language Java virtual machine Java Visual J++ Cross-platform Application programming interface Windows API Computing Software Java platform
636	Comparing the Performance of Man and Machine for TB Screening in Chest Radiographs A. Karargyris, L. Folio, J. Siegelman, F. Callaghan, S. Candemir, Z. Xue, P.-X. Lu, Y.-X. Wang, S. Antani, G. Thoma, S. Jaeger Lister Hil Add to Reading List Source URL: www.lhncbc.nlm.nih.gov Language: English - Date: 2014-08-09 14:21:16 Health in India Chest radiograph Health Tuberculosis Medicine Projectional radiography
637	doi:[removed]j.tics[removed] Add to Reading List Source URL: www.chd.ucsd.edu Language: English - Date: 2011-10-21 18:00:00 Statistical models Computational neuroscience Boltzmann machine Generative model Artificial neural network Supervised learning Wake-sleep algorithm Hidden Markov model Diffusion networks Statistics Neural networks Machine learning
638	Address: Vilnius Gediminas Technical University, Mechanics Faculty, Department of Machine Engineering, J. Basanavičiaus str. 28 a, LT-03224, Vilnius-6 Phones: ([removed], [removed], 9594), Fax: ([removed] Add to Reading List Source URL: www.me.vgtu.lt Language: English - Date: 2012-01-17 13:47:30 Education Higher education Academia Education in the Soviet Union Fırat University University of Waterloo Faculty of Engineering Vilnius Gediminas Technical University Visvesvaraya Technological University Mechanical engineering
639	Mitigating Global Failure Regimes in Large Distributed Systems (Ongoing Research) C. Dabrowski, J. Filliben, K. Mills, S. Ressler and B. Rust from NIST No effective methods exist to predict failure regimes in large distr Add to Reading List Source URL: www.nist.gov Language: English - Date: 2012-09-05 11:18:57 Markov chain Machine learning Probability and statistics Simulation Forward–backward algorithm Perceptron Markov models Statistics Markov processes
640	Mitigating Global Failure Regimes in Large Distributed Systems (Ongoing Research) C. Dabrowski, J. Filliben, K. Mills, S. Ressler and B. Rust from NIST No effective methods exist to predict failure regimes in large distr Add to Reading List Source URL: www.nist.gov Language: English - Date: 2012-06-11 13:51:25 Markov chain Machine learning Probability and statistics Simulation Forward–backward algorithm Perceptron Markov models Statistics Markov processes

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