Welcome to Te-Ming (David) Huang's Homepage
I recently founded the company, Yottamine Analytics, to provide software products and consulting services on predictive analytics. My research interests are machine learning and their application to real world problems such as traffic predictions, digital advertisement, text classification, time series analysis, handwritten recognition, and bioinformatics. Prior to Yottamine Analytics, I was a research scientist at Microsoft and the senior scientist at INRIX where I was specialized in applying my research result to commercial applications, in particular large-scale web classification and real-time traffic prediction. Specifically, I have been developing algorithms for support vector machines and graph-based semi-supervised learning for solving large-scale problems. If you want to know more about me, my full CV is available in PDF format. I am the first author of the monograph Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning which is published by Springer. To the best of my knowledge, it was the first book that treats the fields of supervised, semi-supervised and unsupervised machine learning in a unifying way. If you would like to know more about it, you can also visit the site of the book http://www.learning-from-data.com/.
Huang, T.-M., Kecman, V., Gene Extraction for Cancer Diagnosis using Support Vector Machines- An Improvement, Artificial Intelligence in Medicine (2005) 35, pp.185-194, Special Issue on Computational Intelligence Techniques in Bioinformatics, 2005. Top 25 Articles (Jul -Sep 2005) within AI in Medicine.
Huang, T.-M., Kecman, V., Semi-supervised Learning from Unbalanced Labeled Data – An Improvement, 'International Journal of Knowledge-Based and Intelligent Engineering Systems', Special Issue: Innovational Soft Computing, IOS Press, Vol 10., No. 1, pp. 21-27, 2006.
Huang, T.-M., Kecman, V., Performance Comparisons of Semi-Supervised Learning Algorithms. Proceedings of the Workshop on Learning with Partially Classified Training Data, at the 22nd International Conference on Machine Learning, ICML 2005, W5,pp 45-49, Germany, 2005.
Huang, T.-M., Kecman, V., Gene Extraction for Cancer Diagnosis using Support Vector Machines: An improvement and comparison with nearest shrunken centroid method. Lecture Notes in Computer Science 3696, pp. 617-624, 2005.
Huang, T.-M., Kecman, V., Semi-supervised
Learning from Unbalanced Labeled Data – An Improvement, 'Knowledge
Based and Emergent Technologies Relied Intelligent Information and Engineering
Systems', Eds. Negoita, M. Gh., at al., Lecture Notes in Computer Science
3215, pp. 765-771, Springer Verlag, Heidelberg, 2004. Best Paper Award and Best Student Contribution Paper
Bias Term b in SVMs Again,
12th European Symposium on Artificial Neural Network, ESANN 2004,
pp. 441-448, Bruges, Belgium, April 28-30, 2004.
Kecman, V., Vogt, M., Huang, T.-M., On the Equality of Kernel AdaTron and Sequential Minimal Optimization in Classification and Regression Tasks and Alike Algorithm for Kernel Machines, 11th European Symposium on Artificial Neural Networks, ESANN 2003, pp. 215-222, Bruges, Belgium, April 23-25, 2003.
Huang, T.-M. and Flay, R. G. J., A
Comparison of Recursive Filter and Spectral Methods for Digital Corrected
Pressure Measurements Distorted by Tubing Response, 5th UK
Wind Engineering Society Conference Nottingham, 2002.
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