References
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References
Bennett KP, Campbell C (2000) Support vector machines: hype or hallelujah. Special Interest
Group on Knowledge Discovery in Data Mining Explorations 2(2): 1-13
Bennett KP, Mangasarian OL (1992) Robust linear programming discrimination of two
linearly inseparable sets. Optimization Methods and Software 1: 23-34
Boser H, Guyon 1M, Vapnik VN (1992) A training algorithm for optimal margin classifiers.
Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory,
Pittsburgh, PA, pp 144-152
Campbell C (2002) Kernel methods: a survey of current techniques. Neurocomputing 48:
63-84
Campbell C, Cristianini N (1998) Simple training algorithms for support vector machines.
Technical Report, Bristol University (http://lara.bris.ac.uk/cig)
Cortes C, Vapnik VN (1995) Support vector networks. Machine Learning 20: 273-297
Courant R, Hilbert D (1970) Methods of mathematical Physics, I and II. Wiley Interscience,
New York
Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities
with applications in pattern recognition. IEEE Transaction on Electronic Computers
EC-14: 326-334.
CPLEX Optimization Inc. (1992) CPLEX User's guide, Incline Village, NY
Crammer K, Singer Y (2001) On the algorithmic implementation of multiclass kernel-based
vector machines. Journal of Machine Learning Research 2: 265-292
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other
kernel-based learning methods. Cambridge University Press, Cambridge, UK
Ferris MC, Munson TS (2000a) "Interior point methods for massive support vector machines:' Data Mining Institute Technical Report 00-05, Computer Science Department,
University of Wisconsin, Madion, WI
Ferris MC, Munson TS (2000b) Semi-smooth support vector machines. Data Mining Institute Technical Report 00-09, Computer Science Department, University of Wisconsin,
Madion, WI
Friedman JH (1994) Flexible metric nearest neighbor classification.Technical Report, Department of Statistics, Stanford University
Friedman JH (1996) Another approach to polychotomous classification. Technical Report,
Department of Statistics and Stanford Linear Accelerator Center, Stanford University
Gray RM, Davisson LD (1986) Random processes: a mathematical approach for engineers.
Prentice-Hall, Englewood Cliffs, NJ
Hastie TJ, Tibshirani RJ (1996) Discriminant adaptive nearest neighbor classification. IEEE
Transactions on Pattern Analysis and Machine Intelligence 18(6): 607-615
Hastie TJ, Tibshirani RJ (1998) Classification by pairwise coupling. In: Jordan MI, Kearns
MJ, Solla, SA (eds) Advances in neural information processing systemslO, The MIT
Press, Cambridge, MA, pp 507-513
Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle
River, NJ
Hsu C-W, Lin CJ (2002) A simple decomposition method for support vector machines.
Machine Learning 46: 291-314
Hughes GF (1968) On the mean accuracy of statistical pattern recognizers. IEEE Transactions
on Information Theory 14(1): 55-63
Knerr S, Personnaz L, Dreyfus G (1990) Single-layer learning revisited: A stepwise procedure
for building and training neural network. In: Neurocomputing: algorithms, architectures
and applications, NATO AS I, Springer Verlag, Berlin
155
References
Bennett KP, Campbell C (2000) Support vector machines: hype or hallelujah. Special Interest
Group on Knowledge Discovery in Data Mining Explorations 2(2): 1-13
Bennett KP, Mangasarian OL (1992) Robust linear programming discrimination of two
linearly inseparable sets. Optimization Methods and Software 1: 23-34
Boser H, Guyon 1M, Vapnik VN (1992) A training algorithm for optimal margin classifiers.
Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory,
Pittsburgh, PA, pp 144-152
Campbell C (2002) Kernel methods: a survey of current techniques. Neurocomputing 48:
63-84
Campbell C, Cristianini N (1998) Simple training algorithms for support vector machines.
Technical Report, Bristol University (http://lara.bris.ac.uk/cig)
Cortes C, Vapnik VN (1995) Support vector networks. Machine Learning 20: 273-297
Courant R, Hilbert D (1970) Methods of mathematical Physics, I and II. Wiley Interscience,
New York
Cover TM (1965) Geometrical and statistical properties of systems of linear inequalities
with applications in pattern recognition. IEEE Transaction on Electronic Computers
EC-14: 326-334.
CPLEX Optimization Inc. (1992) CPLEX User's guide, Incline Village, NY
Crammer K, Singer Y (2001) On the algorithmic implementation of multiclass kernel-based
vector machines. Journal of Machine Learning Research 2: 265-292
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other
kernel-based learning methods. Cambridge University Press, Cambridge, UK
Ferris MC, Munson TS (2000a) "Interior point methods for massive support vector machines:' Data Mining Institute Technical Report 00-05, Computer Science Department,
University of Wisconsin, Madion, WI
Ferris MC, Munson TS (2000b) Semi-smooth support vector machines. Data Mining Institute Technical Report 00-09, Computer Science Department, University of Wisconsin,
Madion, WI
Friedman JH (1994) Flexible metric nearest neighbor classification.Technical Report, Department of Statistics, Stanford University
Friedman JH (1996) Another approach to polychotomous classification. Technical Report,
Department of Statistics and Stanford Linear Accelerator Center, Stanford University
Gray RM, Davisson LD (1986) Random processes: a mathematical approach for engineers.
Prentice-Hall, Englewood Cliffs, NJ
Hastie TJ, Tibshirani RJ (1996) Discriminant adaptive nearest neighbor classification. IEEE
Transactions on Pattern Analysis and Machine Intelligence 18(6): 607-615
Hastie TJ, Tibshirani RJ (1998) Classification by pairwise coupling. In: Jordan MI, Kearns
MJ, Solla, SA (eds) Advances in neural information processing systemslO, The MIT
Press, Cambridge, MA, pp 507-513
Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Upper Saddle
River, NJ
Hsu C-W, Lin CJ (2002) A simple decomposition method for support vector machines.
Machine Learning 46: 291-314
Hughes GF (1968) On the mean accuracy of statistical pattern recognizers. IEEE Transactions
on Information Theory 14(1): 55-63
Knerr S, Personnaz L, Dreyfus G (1990) Single-layer learning revisited: A stepwise procedure
for building and training neural network. In: Neurocomputing: algorithms, architectures
and applications, NATO AS I, Springer Verlag, Berlin