156
5: Mahesh Pal, Pakorn Watanachaturaporn
Kohavi R, Foster P (1998) Editorial: glossary of terms. Machine Learning 30:271-274
Kref3el U (1999) Pairwise classification and support vector machines. In: SchiilkopfB, Burges
CJC, Smola AJ (eds), Advances in kernel methods - support vector learning The MIT
Press, Cambridge, MA, pp 255-268
Lee Y, Lin Y, Wahba G (2001) Multicategory support vector machines, Technical Report 1043,
Department of Statistics, University of Wisconsin, Madison, WI
Leunberger D (1984) Linear and nonlinear programming, 2nd edition. Addison-Wesley,
Menlo Park, California
Mangasarian OL, Musicant, DR (1998) Successive over relaxation for support vector machines. Technical Report, Computer Sciences Department, University of Wisconsin,
Madison, Wisconsin.
Mangasarian OL, Musicant DR (2000a) Active support vector machine classification. Technical Report 0004, Data Mining Institute, Computer Sciences Department, University of
Wisconsin, Madison, Wisconsin (ftp: IIftp.cs.wisc.edu/pub/dmi/techreports/0004.ps)
Mangasarian OL, Musicant DR (2000b) Lagrangian support vector machines. Technical
Report 0006, Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin (ftp:llftp.cs.wisc.edu/pub/dmi/techreports/0006.ps)
Mather PM (1999) Computer processing of remotely-sensed images: an introduction 2nd
edition. John Wiley and Sons. Chichester, NY
Mercer J (1909) Functions of positive and negative type, and their connection with the
theory of integral equations. Transactions of the London Philosophical Society (A) 209:
415-446
Minsky ML, Papert SA (1969) Perceptrons. MIT Press, Cambridge, MA
Murtagh BA, Saunders MA (1987) MINOS 5.1 user's guide. (SOL 83-20R), Stanford University.
Osuna EE, Freund R, Girosi F (1997) Support vector machines: training and applications. A. I. Memo No. 1602, CBCL paper No. 144, Artificial Intelligence
laboratory, Massachusetts Institute of Technology, (ftp:llpublications.ai.mit.edu/aipublications/pdfl AIM -1602.pdf)
Pal M (2002) Factors influencing the accuracy of remote sensing classifications: a comparative study. Ph. D. Thesis (unpublished), University of Nottingham, UK
Platt JC (1999) Fast training of support vector machines using sequential minimal optimization. In: SchiilkopfB, Burges qc, Smola AJ (eds), Advances in kernel methodssupport vector learning The MIT Press, Cambridge, MA, pp 185-208
Platt JC, Cristianini N, Shawe-Taylor J (2000) Large margin DAGs for multiclass classification. In: Solla SA, Leen TK, Muller K-R (eds), Advance in neural information processing
systems 12, The MIT Press, Cambridge, MA, pp 547-553
Richards JA, Jia X (1999) Remote sensing digital image analysis: an introduction, 3rd edition.
Springer Verlag, Berlin, Heidelberg, New York
SchiilkopfB (1997) Support vector learning. PhD thesis, Technische Universitat, Berlin.
Schiilkopf B, Smola AJ (2002) Learning with kernels - support vector machines, regularization, optimization and beyond.The MIT Press, Cambridge, MA
Takahashi F, Abe S (2002) Decision-tree-based multiclass support vector machines. Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'02),
3, pp 1418-1422
Tso BCK, Mather PM (2001) Classification methods for remotely sensed data. Taylor and
Francis, London
Vanderbei RJ (1997) User's manual - version 3.10. (SOR-97-08), Statistics and Operations
Research, Princeton University, Princeton, NJ
5: Mahesh Pal, Pakorn Watanachaturaporn
Kohavi R, Foster P (1998) Editorial: glossary of terms. Machine Learning 30:271-274
Kref3el U (1999) Pairwise classification and support vector machines. In: SchiilkopfB, Burges
CJC, Smola AJ (eds), Advances in kernel methods - support vector learning The MIT
Press, Cambridge, MA, pp 255-268
Lee Y, Lin Y, Wahba G (2001) Multicategory support vector machines, Technical Report 1043,
Department of Statistics, University of Wisconsin, Madison, WI
Leunberger D (1984) Linear and nonlinear programming, 2nd edition. Addison-Wesley,
Menlo Park, California
Mangasarian OL, Musicant, DR (1998) Successive over relaxation for support vector machines. Technical Report, Computer Sciences Department, University of Wisconsin,
Madison, Wisconsin.
Mangasarian OL, Musicant DR (2000a) Active support vector machine classification. Technical Report 0004, Data Mining Institute, Computer Sciences Department, University of
Wisconsin, Madison, Wisconsin (ftp: IIftp.cs.wisc.edu/pub/dmi/techreports/0004.ps)
Mangasarian OL, Musicant DR (2000b) Lagrangian support vector machines. Technical
Report 0006, Data Mining Institute, Computer Sciences Department, University of Wisconsin, Madison, Wisconsin (ftp:llftp.cs.wisc.edu/pub/dmi/techreports/0006.ps)
Mather PM (1999) Computer processing of remotely-sensed images: an introduction 2nd
edition. John Wiley and Sons. Chichester, NY
Mercer J (1909) Functions of positive and negative type, and their connection with the
theory of integral equations. Transactions of the London Philosophical Society (A) 209:
415-446
Minsky ML, Papert SA (1969) Perceptrons. MIT Press, Cambridge, MA
Murtagh BA, Saunders MA (1987) MINOS 5.1 user's guide. (SOL 83-20R), Stanford University.
Osuna EE, Freund R, Girosi F (1997) Support vector machines: training and applications. A. I. Memo No. 1602, CBCL paper No. 144, Artificial Intelligence
laboratory, Massachusetts Institute of Technology, (ftp:llpublications.ai.mit.edu/aipublications/pdfl AIM -1602.pdf)
Pal M (2002) Factors influencing the accuracy of remote sensing classifications: a comparative study. Ph. D. Thesis (unpublished), University of Nottingham, UK
Platt JC (1999) Fast training of support vector machines using sequential minimal optimization. In: SchiilkopfB, Burges qc, Smola AJ (eds), Advances in kernel methodssupport vector learning The MIT Press, Cambridge, MA, pp 185-208
Platt JC, Cristianini N, Shawe-Taylor J (2000) Large margin DAGs for multiclass classification. In: Solla SA, Leen TK, Muller K-R (eds), Advance in neural information processing
systems 12, The MIT Press, Cambridge, MA, pp 547-553
Richards JA, Jia X (1999) Remote sensing digital image analysis: an introduction, 3rd edition.
Springer Verlag, Berlin, Heidelberg, New York
SchiilkopfB (1997) Support vector learning. PhD thesis, Technische Universitat, Berlin.
Schiilkopf B, Smola AJ (2002) Learning with kernels - support vector machines, regularization, optimization and beyond.The MIT Press, Cambridge, MA
Takahashi F, Abe S (2002) Decision-tree-based multiclass support vector machines. Proceedings of the 9th International Conference on Neural Information Processing (ICONIP'02),
3, pp 1418-1422
Tso BCK, Mather PM (2001) Classification methods for remotely sensed data. Taylor and
Francis, London
Vanderbei RJ (1997) User's manual - version 3.10. (SOR-97-08), Statistics and Operations
Research, Princeton University, Princeton, NJ