Machine Learning

Publications:

Links:

Learning resources:

 

Publications:

Papers:  (Available as .pdf unless otherwise specified) -

Ensemble machine methods for DNA binding (with Y. Fan, and C. DeLisi),  Machine Learning and Applications 7,  M. Wani, et al., eds.  IEEE, Washington (2008),709-716.  Algorithm available here.

 

Regulatory analysis for exploring human disease progression (with D. Holloway and C. DeLisi), Biology Direct 3:24, 2008. Algorithm available here.

 

Building transcription factor classifiers and discovering relevant biological features,  (with D. Holloway and C. DeLisi), BiologyDirect 3:22, 30 May 2008. Algorithm available here.

 

SVMMotif:  A machine learning motif algorithm (with Y. Fan, D.Holloway and C. DeLisi), International Conference on Machine Learning and Applications 6, 573-580, IEEE, Washington, 2007. Algorithm available here.

 

Learning methods for DNA binding in computational biology (with D. Holloway, et al.) International Joint Conference on Neural Networks,  20, IEEE, Los Alamitos 1605, 2007.

 

Machine learning for regulatory analysis and transcription factor target prediction in yeast (with D. Holloway and C. DeLisi), Systems and Synthetic Biology 1 (2006), 25-46.                                                                                                              

 

Approximating functions in reproducing kernel Hilbert spaces via statistical learning theory (with L. Raphael), in Splines and Wavelets, G. Chen and M.J. Lai, eds, (2006) 270-286

Machine learning methods for transcription data integration (with D. Holloway and C.DeLisi),IBM Journal of Research and Development 50(2006), 631-644 (Abstract only - Journal link is here)

Information-based nonlinear approximation:  An average case setting (with L. Plaskota),  J. Complexity 21 (2005),211-228.

Extending Girosi's approximation estimates for functions in Sobolev spaces via statistical learning theory (with L. Raphael and D. Williams), J. Analysis and Applications 3 No. 2 (2005), 67-90.

Statistical likelihood representations of prior knowledge in machine learning (with L, Plaskota and A. Przybyszewski), Artificial Intelligence and Applications, M.H. Hamza, Ed., Innsbruck (2005), 467-472.

Integrating genomic data to predict transcription factor binding  (with D. Holloway and C. DeLisi), Genome Informatics 16 (2005), 83-94.

Machine learning and statistical MAP methods (with L. Plaskota and A. Przybyszewski), Intelligent Information Processing, Springer, Berlin (2005), 441-445.

On some integrated approaches to inference (with L. Plaskota), technical report, (2005).

Complexity of predictive neural networks (with L. Plaskota) Proceedings of International Conference on Complexity,  Y. Bar-Yam, Ed., Cambridge, MA (2003)

Complexity of neural network approximation with limited information:  a worst-case approach (with L. Plaskota), J. Complexity 17 (2001), 345-365.

Complexity of regularization RBF networks (with L. Plaskota), in Proceedings of International Joint Congress on Neural Networks,INNS, Washington (2001), 342-346.

Review of Complexity and Information, (by J.F. Traub and A.G. Werschulz), Bull. Amer. Math Soc. 37 (2000), 199-204.

Information complexity of neural networks (with L. Plaskota), Neural Networks 13 (2000),365-376.

Neural networks, radial basis functions, and complexity (with L. Plaskota), Proceedings of Bialowieza Conference on Statistical Physics, 1997, 122-145.

Some Links:

Journal of Machine Learning Research

Cambridge (UK) Machine Learning Group

Princeton Machine Learning Group

CMU Machine Learning Department

Tom Mitchell ML Book

 

Learning Resources:

Resources:  General Machine Learning (ML):

     I.  Elementary Resources (definitions, tutorials, basics):

         ML:

               http://en.wikipedia.org/wiki/Machine_learning

               http://old-site.clsp.jhu.edu/workshops/ws09/documents/machine-learning-overview.pdf

                http://mlthirst.wordpress.com/2013/01/08/machine-learning-guidance-for-beginners/

                http://machinelearningmastery.com/self-study-guide-to-machine-learning/

                http://www.werc.tu-darmstadt.de/fileadmin/user_upload/GROUP_WERC/LKE/tutorials/ML-tutorial-1-2.pdf

                 http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

         SVM:

               http://en.wikipedia.org/wiki/Support_vector_machine

                  SVM Lecture Notes – MA 770 (Kon)

                  Applications of SVM – MA 770

 

    II.  Mid-level Resources:

         ML:

               http://alex.smola.org/teaching/cmu2013-10-701x/

                http://dimacs.rutgers.edu/Workshops/MachineLearning/slides/schapire.pdf

                   http://www.cs.uvm.edu/~icdm/algorithms/10Algorithms-08.pdf

             Stanford course:

                http://cs229.stanford.edu

                https://www.coursera.org/course/ml

                http://see.stanford.edu/see/lecturelist.aspx?coll=348ca38a-3a6d-4052-937d-cb017338d7b1

        SVM:

                Mathematics of SVM – MA 770

                http://videolectures.net/epsrcws08_campbell_isvm/

                http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf

http://videolectures.net/Top/Computer%5FScience/Machine%5FLearning/Kernel%5FMethods/Support%5FVector%5FMachines/

                http://pyml.sourceforge.net/doc/howto.pdf

       Random Forests (RF):

                  RF Lecture Notes – MA 770 (Kon)

                  Mathematics of RF – MA 770

       k-nearest neighbors (kNN):

                   kNN lecture notes (Kon)

      III.  Advanced Resources

         ML:

              http://alex.smola.org/teaching/cmu2013-10-701x/

              http://ai.stanford.edu/~nilsson/MLBOOK.pdf

              http://www.mit.edu/~9.520/

              http://www-stat.stanford.edu/~tibs/ElemStatLearn/

         SVM:

               http://www.support-vector-machines.org

               http://webee.technion.ac.il/people/rmeir/SVMReview.pdf

               http://www.support-vector.net/LeuvenKerMet-2.pdf

              http://www.mit.edu/~9.520/spring11/slides/class06-svm.pdf

              http://www.stanford.edu/class/cs229/notes/cs229-notes3.pdf

 

          RF:

                 http://stat-www.berkeley.edu/users/breiman/RandomForests/cc_home.htm#workings