Pattern Recognition & Machine Learning

General

• Langley P. (1988) "Machine Learning as an Experimental Science" Machine Learning, 3(1): 5-8.
• Dietterich T.G. (1990) "Exploratory Research in Machine Learning" Machine Learning, 5(1): 5-10.
• Langley P. (1990) "Advice to Machine Learning Authors" Machine Learning, 5(3): 233-237.
• Mitchell T.M. (1997) "Does Machine Learning Really Work?" AI Magazine, 18(3): 11-20. 
• Dietterich T.G. (1997) "Machine Learning Research: Four Current Directions" AI Magazine, 18(4): 97-136.
• Provost F. and Kohavi R. (1998) "On Applied Research in Machine Learning" Machine Learning, 30(2/3): 127-132.
• Langley P. (2000) "Crafting Papers on Machine Learning" Proc. of the 17th Int. Conf. on Machine Learning, pp. 1207-1211.
• Jain A.K., Duin R.P.W., and Mao J. (2000) "Statistical pattern recognition: A review" IEEE Transactions on Pattern Analysis and Machine Intelligence, 
  22(1): 4-37.
• Domingos P. (2002) "Machine Learning" in Handbook of Data Mining and Knowledge Discovery, Klosgen W. and Zytkow J. (Editors),
  New York, NY: Oxford University Press, pp. 660-670.
• Pavlidis T. (2003) "36 years on the pattern recognition" Pattern Recognition Letters, 24(1/3): 1-7.

Clustering

• Jain A.K. and Dubes R.C. (1988) "Algorithms for Clustering Data" Prentice Hall.
• Yang M.S. (1993) "A Survey of Fuzzy Clustering" Mathematical and Computer Modelling, 18(11): 1-16.
• Jain A.K., Murty M.N., and Flynn P.J. (1999) "Data Clustering: A Review" ACM Computing Surveys, 31(3): 264-323.
• Baraldi A. and Blonda P. (1999) "A Survey of Fuzzy Clustering Algorithms for Pattern Recognition I & II" 
  IEEE SMC-B, 29(6): 778-801. [Also available as Technical Report TR-98-039]
• Fung G. (2001) "A Comprehensive Overview of Basic Clustering Algorithms" 
• Berkhin P. (2002) "Survey of Clustering Data Mining Techniques"
• Mercer D.P. (2003) "Clustering Large Datasets" Report

Feature Extraction

• Siedlecki W., Siedlecka K., and Sklansky J. (1988) "An Overview of Mapping Techniques for Exploratory Pattern Analysis" 
  Pattern Recognition, 21(5): 411-429.
• Carreira-Perpiñán M.Á. (1997) "A Review of Dimension Reduction Techniques" Technical Report CS-96-09, 
  Dept. of Computer Science, University of Sheffield
• Konig A. (2001) "Dimensionality Reduction Techniques for Interactive Visualization, Exploratory Data Analysis, and Classification" 
  in Pattern Recognition in Soft Computing Paradigm, Nikhil R. Pal (Ed.), Singapore, World Scientific Publishing Co., pp. 1-37.
• Fodor I.K. (2002) "A Survey of Dimension Reduction Techniques" Lawrence Livermore National Laboratory Technical Report UCRL-ID-148494

Feature (Subset) Selection

• Ferri F.J., Pudil P., Hatef M., and Kittler J. (1994) "Comparative Study of Techniques for Large-Scale Feature Selection" in Pattern Recognition in Practice IV, Multiple Paradigms, Comparative Studies and Hybrid Systems, E.S. Gelsema and L.N. Kanal (Eds.), Elsevier, Amsterdam, Nederlands, pp. 403-413.
• Aha D.W. and Bankert R.L. (1995) "A Comparative Evaluation of Sequential Feature Selection Algorithms" Proc. of the 5th Int. Workshop 
  on Artificial Intelligence and Statistics, Ft. Lauderdale, FL., pp. 1-7.
• Jain A.K. and Zongker D. (1996) "Algorithms for Feature Selection: An Evaluation" Proc. of the IEEE Int. Conf. on Pattern Recognition, pp. 18-22.
• Jain A.K. and Zongker D. (1997) "Feature Selection: Evaluation, Application, and Small Sample Performance"
  IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2): 153-158.
• Dash M. and Liu H. (1997) "Feature Selection for Classification" Intelligent Data Analysis, 1(3): 131-156.
• Liu H. and Matoda H. (1998) "Feature Selection for Knowledge Discovery and Data Mining" Kluwer Academic Publishers
• Kudo M. and Sklansky J. (2000) "Comparison of Algorithms that Select Features for Pattern Classifiers" Pattern Recognition, 33(1): 25-41.
• Nebot A., Belanche L., and Molina L.C. (2002) "Feature Selection Algorithms: A Survey and Experimental Evaluation" Technical Report LSI-02-62-R
• Somol P. and Pudil P. (2002) "Feature Selection Toolbox" Pattern Recognition, 35(12): 2749-2759.
• Guyon I. and Elisseeff A. (2003) "An Introduction to Variable and Feature Selection" Journal of Machine Learning Research, 3: 1157-1182.
• Liu H. and Yu. L. (2005) "Toward Integrating Feature Selection Algorithms for Classification and Clustering" 
  IEEE Transactions on Knowledge and Data Engineering, 17(4): 491-502.

Genetic Algorithms

• Beasley D., Bull D.R., and Martin R.R. (1993) "An Overview of Genetic Algorithms: Part 1, Fundamentals" University Computing, 15(2): 58-69.
• Beasley D., Bull D.R., and Martin R.R. (1993) "An Overview of Genetic Algorithms: Part 2, Research Topics" University Computing, 15(4): 170-181.
• Whitley D. (1994) "A Genetic Algorithm Tutorial" Statistics and Computing, 4: 65-85.
• Srinivas M. and Patnaik L.M. (1994) "Genetic Algorithms: A Survey" IEEE Computer, 27(6): 17-26.
• Koza J.R. (1995) "Survey of Genetic Algorithms and Genetic Programming" Proc. of 1995 WESCON Conference, pp. 589-594.
• Whitley D. (1996) "Genetic Algorithms and Neural Networks" in Genetic Algorithms in Engineering and Computer Science, G. Winter, J. Periaux, M. Galan and P. Cuesta (Eds.), John Wiley & Sons, pp. 203-216.
• Michalewicz Z., Hinterding R., and Michalewicz M. (1997) "Evolutionary Algorithms" in Fuzzy Evolutionary Computation, W. Pedrycz (Ed.), 
  Kluwer Academic Publishers
• Unger T. (1998) "Genetic Algorithms: A Survey of Some Mathematical Models- Part 1" Irish Mathematical Society Bulletin, 41: 57-71.
• Michalewicz Z., Deb K., Schmidt M., Stidsen T.J. (1999) "Evolutionary algorithms for engineering applications" in Evolutionary Algorithms in Engineering and Computer Science, K. Miettinen, M.M. Mäkelä, P. Neittaanmäki, J. Périaux (Eds.), John Wiley & Sons, pp. 73-94.
• Whitley D. (2001) "An Overview of Evolutionary Algorithms: Practical Issues and Common Pitfalls" J. of Information and Software Technology, 43: 817-831.
• Hsiao J.H. (2001) "Genetic Algorithms and Its Application to Constraint Satisfaction Problems" Report
• Whitley D. (2002) "Genetic Algorithms and Evolutionary Computing" Van Nostrand's Scientific Encyclopedia
• Eiben A.E. (2002) "Evolutionary computing: the most powerful problem solver in the universe?" Dutch Mathematical Archive, 5/3(2): 126-131.
• Dianati M., Song I., and Treiber M. "An Introduction to Genetic Algorithms and Evolutionary Strategies" Technical Report, 
  Dept. of Electrical and Computer Engineering, University of Waterloo

Support Vector Machines

• Burges C.J.C. (1998) "A Tutorial on Support Vector Machines for Pattern Recognition" Data Mining and Knowledge Discovery, 2(2): 121-167.
• Bennett K.P. and Campbell C. (2000) "Support Vector Machines: Hype or Hallelujah?" SIGKDD Explorations, 2(2): 1-13.
• Muller K.-R. Mika S., Ratsch G., Tsuda K., and Scholkopf B. (2001) "An Introduction to Kernel-Based Learning Algorithms" 
  IEEE Transactions on Neural Networks, 12(2): 181-201.

Class Imbalance

• Provost F. "Learning with Imbalanced Data Sets 101" Proc. of the AAAI'2000 Workshop on Imbalanced Data Sets
• Elkan C. (2001) "The Foundations of Cost-Sensitive Learning" Proc. of the 17th Int. Joint Conf. on Artificial Intelligence (IJCAI'01), pp. 973-978.
• Laurikkala J. (2001) "Improving Identification of Difficult Small Classes by Balancing Class Distribution" Technical Report A-2001-2. 
  Dept. of Computer and Information Sciences. University of University of Tampere.
• Japkowicz N. and Stephen S. (2002) "The Class Imbalance Problem: A Systematic Study" Intelligent Data Analysis 6(5): 429-449.
• Weiss G.M. and Provost F. (2003) "Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction" 19: 315-354.
• Chawla N.V., Japkowicz N., and Kotcz A. (2004) "Editorial: Special Issue on Learning from Imbalanced Data Sets" SIGKDD Explorations, 6(1): 1-6.
• Barandela R., Sánchez J.S., García V., and Rangel E. (2003) "Strategies For Learning in Class Imbalance Problems" Pattern Recognition, 36(3): 849-851.
• Batista G.E.A.P.A., Prati R.C., and Monard M.C. (2004) "A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data" SIGKDD Explorations, 6: 20-29
• Barandela R., Valdovinos R.M., Sánchez J.S., and Ferri F.J. (2004) "The Imbalanced Training Sample Problem: Under or Over Sampling?" 
  Lecture Notes in Computer Science, 3138: 806-814.
• Weiss G.M. (2004) "Mining with Rarity: A Unifying Framework" SIGKDD Explorations, 6(1): 7-19.

ROC Curve & AUC 

• Hanley, J.A. and McNeil, B.J. (1982) "The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve" Radiology 143(1): 29-36.
• Swets J.A. (1988) "Measuring the Accuracy of Diagnostic Systems" Science, 240(4857): 1285-1293.
• Bradley A.P. (1997) "The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms" Pattern Recognition, 30(7): 1145-1159.
• Swets J.A., Dawes R.M., and Monaham J. (2000) "Better Decision through Science" Scientific American, 283: 82-87.
• Fawcett T. (2003) "ROC Graphs: Notes and Practical Considerations for Researchers" HP Labs Technical Report HPL-2003-4
• Marzban C. (2004) "The ROC Curve and the Area Under it as a Performance Measure" Weather and Forecasting, 19(6): 1106-1114.
• Huang J. and Ling C.X. (2005) "Using AUC and Accuracy in Evaluating Learning Algorithms" IEEE Transactions on Knowledge and Data Engineering, 
  17(3): 299-310.