Computational Learning Theory
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Guyon, I., Boser, B., Vapnik, V.
He is now at the University of California, Berkeley.
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Guyon, I., Boser, B., Vapnik, V.
He is now at the University of California, Berkeley.
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Guyon, I., Boser, B., Vapnik, V.
He is now at the University of California, Berkeley.
Information, Prediction, and Query by Committee
Freund, Yoav, Seung, H. Sebastian, Shamir, Eli, Tishby, Naftali
We analyze the "query by committee" algorithm, a method for filtering informativequeries from a random stream of inputs. We show that if the two-member committee algorithm achieves information gainwith positive lower bound, then the prediction error decreases exponentially with the number of queries. We show that, in particular, this exponential decrease holds for query learning of thresholded smooth functions.
The VC-Dimension versus the Statistical Capacity of Multilayer Networks
The former characterizes their "Present Address: Department of Electrical Computer and System Engineering, Rensselaer Poly tech Institute, Troy, NY 12180.
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods
Haussler, David, Kearns, Michael, Opper, Manfred, Schapire, Robert
In this paper we investigate an average-case model of concept learning, and give results that place the popular statistical physics and VC dimension theories of learning curve behavior in a common framework.
The VC-Dimension versus the Statistical Capacity of Multilayer Networks
The former characterizes their "Present Address: Department of Electrical Computer and System Engineering, Rensselaer Poly tech Institute, Troy, NY 12180.
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods
Haussler, David, Kearns, Michael, Opper, Manfred, Schapire, Robert
In this paper we investigate an average-case model of concept learning, and give results that place the popular statistical physics and VC dimension theories of learning curve behavior in a common framework.
The VC-Dimension versus the Statistical Capacity of Multilayer Networks
The former characterizes their "Present Address: Department of Electrical Computer and System Engineering, Rensselaer Polytech Institute, Troy, NY 12180.
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods
Haussler, David, Kearns, Michael, Opper, Manfred, Schapire, Robert
In this paper we investigate an average-case model of concept learning, and give results that place the popular statistical physics and VC dimension theories of learning curve behavior in a common framework.