-
Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks (Information Science and Statistics)
(Robert G. Cowell, Philip Dawid, Steffen L. Lauritzen, David J. Spiegelhalter / Springer)
-
Graphical Models: Foundations of Neural Computation (Computational Neuroscience)
( / The MIT Press)
-
Learning in Graphical Models (Adaptive Computation and Machine Learning series)
( / A Bradford Book)
-
Markov Random Field Modeling in Image Analysis (Computer Science Workbench)
(S. Z. Li / Springer-Verlag)
-
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
(Bernhard Schölkopf, Alexander J. Smola / The MIT Press)
-
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
(Nello Cristianini, John Shawe-Taylor / Cambridge University Press)
-
サポートベクターマシン入門
(ネロ クリスティアニーニ, ジョン ショー‐テイラー / 共立出版)
-
The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer Series in Statistics)
(Trevor Hastie, Robert Tibshirani, Jerome H. Friedman / Springer-Verlag)
-
Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations (The Morgan Kaufmann Series in Data Management Systems)
(Ian H. Witten, Eibe Frank / Morgan Kaufmann)