Support vector machine - PDFSEARCH.IO - Document Search Engine

Support vector machine
Results: 2011

#	Item
581	Steganalysis using Logistic Regression Ivans Lubenko and Andrew D. Ker Oxford University Computing Laboratory, Parks Road, Oxford OX1 3QD, England. ABSTRACT We advocate Logistic Regression (LR) as an alternative to the S Add to Reading List Source URL: www.cs.ox.ac.uk Language: English - Date: 2010-12-25 07:20:09 Steganalysis Linear classifier Multiclass classification Multinomial logit Linear discriminant analysis Steganography Logistic regression Stability Binary classification Statistics Statistical classification Support vector machine
582	Microsoft Word - 2013ISId.docx Add to Reading List Source URL: www.statistics.gov.hk Language: English - Date: 2013-08-22 04:39:13 Regression analysis Econometrics Machine learning Linear discriminant analysis Dummy variable Cross-validation Logistic regression Support vector machine SPSS Statistics Statistical classification Multivariate statistics
583	UNIVERSITY OF CALIFORNIA, SAN DIEGO More like this: machine learning approaches to music similarity A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Add to Reading List Source URL: bmcfee.github.io Language: English - Date: 2015-03-31 11:15:28 Machine learning Science Multivariate statistics Natural language processing Statistical classification Learning to rank Support vector machine Dimension reduction Recall Statistics Information science Information retrieval
584	A Course in Machine Learning Hal Daumé III 9 \| K ERNEL M ETHODS Add to Reading List Source URL: ciml.info Language: English - Date: 2014-09-24 16:08:31 Vectors Kernel trick Perceptron Function Vector space Support vector machine Positive-definite kernel Golden ratio base Pullback Algebra Mathematics Operator theory
585	Forecasting of building settlements due to earthquake liquefaction based on LSSVM with mixed kernel Fei Wang* Institute of Earthquake Resistance and Disaster Reduction, Beijing University of Technology, Beijing, C Add to Reading List Source URL: www.ejge.com Language: English - Date: 2015-01-01 12:00:01 Statistical classification Support vector machines Least squares support vector machine Stability Vector space Positive-definite kernel Algebra Statistics Mathematics
586	From Region Similarity to Category Discovery Carolina Galleguillos† Brian McFee† Serge Belongie† Gert Lanckriet‡ † Computer Science and Engineering Department Add to Reading List Source URL: bmcfee.github.io Language: English - Date: 2015-03-31 11:15:28 K-nearest neighbor algorithm Supervised learning Support vector machine Large margin nearest neighbor Semidefinite embedding Statistical classification Spectral clustering Positive-definite kernel Statistics Machine learning Artificial intelligence
587	Thinking about Abstracts John Carlis Computer Science and Engineering, CS&E Biomedical Informatics and Computational Biology, BICB University of Minnesota Add to Reading List Source URL: wac.colostate.edu Language: English - Date: 2014-08-24 08:40:49 Mathematical sciences Multiclass classification Synthetic data Structure Learning Statistical classification Hierarchy Support vector machine
588	Max-margin Methods Notation Notation Example images (v1T, v2T,…,v5T)T, where Add to Reading List Source URL: users.cecs.anu.edu.au Language: English - Date: 2009-06-21 14:47:09 Machine learning Support vector machines Information theory Differential geometry Least squares support vector machine Statistics Statistical classification Hinge loss
589	Baselines and Bigrams: Simple, Good Sentiment and Topic Classification Sida Wang and Christopher D. Manning Department of Computer Science Stanford University Stanford, CA 94305 {sidaw,manning}@stanford.edu Add to Reading List Source URL: nlp.stanford.edu Language: English - Date: 2012-05-24 23:21:00 Statistical classification Natural language processing Support vector machine Learning Document classification Cross-validation Naive Bayes classifier Active learning Sentiment analysis Statistics Machine learning Artificial intelligence
590	Coal Seam Thickness Prediction based on Least Squares Support Vector Machines and Kriging Method Chen Youkuo School of Resource and Earth Science, Key Laboratory of CBM Resources and Reservoir Formation Process of Minist Add to Reading List Source URL: www.ejge.com Language: English - Date: 2015-01-03 20:12:27 Regression analysis Multivariate interpolation Statistical classification Variogram Kriging Least squares support vector machine Support vector machine Linear interpolation Linear regression Statistics Geostatistics Interpolation

UPDATE