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Paper Reading Notes[edit]

Data mining and machine learning

Xindong Wu, Vinpin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, Dan Steinberg. Top 10 algorithms in data mining, IEEE International Conference on Data Mining (ICDM), 2006. C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Description, the impact, current and future research of each algorithm are reviewed in this paper.

Nearest neighbor finding algorithm in d-dimensional space

Alexandr Andoni, Piotr Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In Proceedings of the Symposium on Foundations of Computer Science (FOCS'06), 2006. An algorithm based on locality sensitive hashing for the c-approximate nearest neighbor finding problem in a d-dimensional Euclidean space is presented. It achieves query time of O(d*n^(1/c^2+o(1)) and space O(d*n+n^(1+1/c^2+o(1))). Another space-efficient version is also described with d*n+n*log^O(1)(n) space and query time of d*n^O(1/c^2). See papers and implementation on Locality Sensitive Hashing.

Semi-supervised learning

Tutorial by Xiaojin Zhu @ University of Wisconsin, Madison. Literature Survey by Xiaojin Zhu @ University of Wisconsin, Madison.

Combining labeled and unlabeled data.

Resource Collections[edit]

Resource Collection - Machine Learning

Resource Collection - Information Retrieval


Machine Learning/Learning Theory Hotlists by Stephen D. Scott @ University of Nebraska.

Zuobin Ghahramani, homepage @ University of Cambridge, video lectures.

Xiaojin Zhu, Jerry, homepage @ University of Wisconsin, Madison.

Dengyong Zhou, Denny, homepage @ Microsoft Research.

Chris Burges, homepage @ Microsoft Research.

Sam Roweis, homepage @ Dept. Computer Science, University of Toronto.

Olivier Chapelle, homepage @ Yahoo! Research.

Richard O. Duda, homepage @ Artificial Intelligence Center, SRI.

Tobias Scheffer, homepage @ Max-Planck-Institut fur Informatik.

Groups / Institutes[edit]

Machine Learning Department, School of Computer Science, Carnegie Mellon].

Lectures / Seminars[edit]

Machine Learning Seminars @ Cambridge University Engineering Department.

Hotlists / Blogs / Forums / Mail Lists[edit]

Support Vector Machines (SVMs). Support Vector Machine.

Machine Learning (Theory).

Statistical Data Mining Tutorials by Andrew Moore @ Google & CMU. "The following links point to a set of tutorials on many aspects of statistical data mining, including the foundations of probability, the foundations of statistical data analysis, and most of the classic machine learning and data mining algorithms."


International Conference on Machine Learning (ICML), 2008, 2007;



Groups / Institutes[edit]

Lectures / Seminars[edit]

Music and Machine Learning.

Hotlists / Blogs / Forums / Mail Lists[edit]


ACM International Conference on Multimedia (ACM-MM), 2008;

International ACM SIGIR Conference (ACM-SIGIR), 2008;

ACM International Conference on Multimedia Information Retrieval (ACM-MIR), 2008;


IEEE Transactions on Multimedia (TMM);

Computer Music Journal (CMJ);