Papers: (Available as .pdf unless otherwise specified) -
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.
likelihood representations of prior knowledge in machine learning (with L, Plaskota and A. Przybyszewski), Artificial Intelligence and Applications,
M.H. Hamza, Ed.,
Integrating genomic data to predict transcription factor binding (with D. Holloway and C. DeLisi), Genome Informatics 16 (2005), 83-94.
learning and statistical MAP methods (with L. Plaskota and A. Przybyszewski),
Intelligent Information Processing, Springer,
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 of neural network approximation with limited
information: a worst-case approach
(with L. Plaskota), J. Complexity 17
Complexity of regularization RBF networks (with L. Plaskota), in Proceedings of International Joint
Congress on Neural Networks,INNS,
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.
Resources: General Machine Learning (ML):
I. Elementary Resources (definitions, tutorials, basics):
II. Mid-level Resources:
Random Forests (RF):
k-nearest neighbors (kNN):
III. Advanced Resources