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291	Accuracy of the s-step Lanczos method for the symmetric eigenproblem Erin Carson James Demmel Add to Reading List Source URL: www.eecs.berkeley.edu Language: English - Date: 2014-09-17 14:12:22 Linear algebra Lanczos algorithm Krylov subspace Generalized minimal residual method Cornelius Lanczos Iterative method Conjugate gradient method Tridiagonal matrix Eigenvalues and eigenvectors Algebra Numerical linear algebra Mathematics
292	Improving FPGA Accelerated Tracking with Multiple Online Trained Classifiers Matthew Jacobsen, Siddarth Sampangi, Yoav Freund, Ryan Kastner Computer Science & Engineering University of California, San Diego {mdjacobs, ss Add to Reading List Source URL: kastner.ucsd.edu Language: English - Date: 2014-06-30 10:31:01 Computational statistics Machine learning Parts of speech Statistical classification AdaBoost Haar-like features Classifier Boosting Random subspace method Ensemble learning Artificial intelligence Learning
293	SUPPORT VECTOR MACHINE AND MAXIMUM LIKELIHOOD APPROACHES TO F-MEASURE OPTIMIZATION Trevor Rose Supervisor: A/ Prof. Spiridon Penev Add to Reading List Source URL: www.ontotext.com Language: English - Date: 2014-08-19 16:38:16 Linear classifier Support vector machine Binary classification Principle of maximum entropy Automatic summarization Margin classifier Random subspace method Statistics Statistical classification Machine learning
294	Scaling Up Crowd-Sourcing to Very Large Datasets: A Case for Active Learning Barzan Mozafari⋆ ⋆ Purna Sarkar† Michael Franklin† Michael Jordan† Samuel Madden‡ Add to Reading List Source URL: www.vldb.org Language: English - Date: 2014-09-15 15:12:31 Statistical classification Bootstrapping Resampling Support vector machine Classifier Margin Random subspace method Classifier chains Statistics Machine learning Active learning
295	Universal quantum simulation with pre-threshold superconducting qubits: Single-excitation subspace method Michael R. Geller,1, ∗ John M. Martinis,2 Andrew T. Sornborger,3 Phillip C. Stancil,1 Emily J. Pritchett,4 and A Add to Reading List Source URL: web.physics.ucsb.edu Language: English - Date: 2013-01-10 15:11:54 Applied mathematics Quantum computer Qubit Quantum circuit Quantum algorithm Toric code Quantum information BQP Quantum decoherence Theoretical computer science Quantum information science Quantum mechanics
296	Institut für Informatik und Wirtschaftsinformatik -- Lehrstuhl für Wirtschaftsinformatik V Dr. Heiko Paulheim / Robert Meusel Exercise 4: Data Mining II – Ensembles In this exercise we will focus on all kinds of ense Add to Reading List Source URL: dws.informatik.uni-mannheim.de Language: English - Date: 2014-09-29 11:42:11 Learning Classifier Boosting AdaBoost Statistical classification Random subspace method Cascading classifiers Ensemble learning Statistics Artificial intelligence
297	1 RANDOM FORESTS Leo Breiman Statistics Department Add to Reading List Source URL: www.stat.berkeley.edu Language: English - Date: 2001-12-12 14:25:29 AdaBoost Confidence interval Boosting Variance Random subspace method Statistics Ensemble learning Random forest
298	Named Entity Recognition through Classifier Combination Radu Florian and Abe Ittycheriah and Hongyan Jing and Tong Zhang IBM T.J. Watson Research Center 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA {raduf,abei,hjin Add to Reading List Source URL: www.cnts.ua.ac.be Language: English - Date: 2003-06-04 15:50:31 Statistical classification Linear classifier Classifier Ensemble learning Part-of-speech tagging Multinomial logit Random subspace method Boosting methods for object categorization Statistics Machine learning Artificial intelligence
299	Mining Disorder Attributes with Rules and Statistical Learning Xiaohua Liu, Xiaojie Liu, Wei Shen, and Jianyun Nie DIRO, Universit de Montral, H3C 3J7, Qubec, Canada liuxiao,xiaojie,shenwei,[removed] Add to Reading List Source URL: ceur-ws.org Language: English - Date: 2014-07-24 06:47:16 Parts of speech Classifier Unified Modeling Language Support vector machine Bayesian statistics Random subspace method Classification rule Statistics Statistical classification Machine learning
300	nce upon a time, digital information was scarce. No more. According to IBM we create 2.5 quintillion bytes of data each day. Ninety percent of the digital data in the world right now was created in the past two years. Add to Reading List Source URL: www.sandia.gov Language: English - Date: 2013-10-07 18:46:16 Diffusion MRI Symmetric tensor Statistics Array data type Multilinear algebra Multilinear subspace learning Higher-order singular value decomposition Tensors Mathematics Physics

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