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Home > Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012, Proceedings > Application of Bagging, Boosting and Stacking to Intrusion Detection
Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012, Proceedings |
Petra Perner
Springer Berlin Heidelberg
Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012, Proceedings
Application of Bagging, Boosting and Stacking to Intrusion Detection
Authors

Iwan Syarif; Ed Zaluska; Adam Prugel-Bennett; Gary Wills

ARI Id

1664841294623_3738619

Access

Not Available Free

Pages

593-602

DOI

10.1007/978-3-642-31537-4_46

Chapter URL

https://rd.springer.com/chapter/10.1007/978-3-642-31537-4_46

Table of Contents of Book
Showing 1 to 20 of 53 entries
Chapters/HeadingsAuthor(s)PagesInfo
Front Matter
Bayesian Approach to the Concept Drift in the Pattern Recognition Problems
Pavel Turkov; Olga Krasotkina; Vadim Mottl
1-10
Transductive Relational Classification in the Co-training Paradigm
Michelangelo Ceci; Annalisa Appice; Herna L. Viktor; Donato Malerba; Eric Paquet; Hongyu Guo
11-25
Generalized Nonlinear Classification Model Based on Cross-Oriented Choquet Integral
Rong Yang; Zhenyuan Wang
26-39
A General Lp-norm Support Vector Machine via Mixed 0-1 Programming
Hai Thanh Nguyen; Katrin Franke
40-49
Reduction of Distance Computations in Selection of Pivot Elements for Balanced GHT Structure
László Kovács
50-62
Hot Deck Methods for Imputing Missing Data
Dieter William Joenssen; Udo Bankhofer
63-75
BINER
Saket Bharambe; Harshit Dubey; Vikram Pudi
76-85
A New Approach for Association Rule Mining and Bi-clustering Using Formal Concept Analysis
Kartick Chandra Mondal; Nicolas Pasquier; Anirban Mukhopadhyay; Ujjwal Maulik; Sanghamitra Bandhopadyay
86-101
Top-N Minimization Approach for Indicative Correlation Change Mining
Aixiang Li; Makoto Haraguchi; Yoshiaki Okubo
102-116
Selecting Classification Algorithms with Active Testing
Rui Leite; Pavel Brazdil; Joaquin Vanschoren
117-131
Comparing Logistic Regression, Neural Networks, C5. 0 and M5′ Classification Techniques
Amit Thombre
132-140
Unsupervised Grammar Inference Using the Minimum Description Length Principle
Upendra Sapkota; Barrett R. Bryant; Alan Sprague
141-153
How Many Trees in a Random Forest?
Thais Mayumi Oshiro; Pedro Santoro Perez; José Augusto Baranauskas
154-168
Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropy
Tao Xu; David Chiu; Iker Gondra
169-182
A New Learning Structure Heuristic of Bayesian Networks from Data
Heni Bouhamed; Afif Masmoudi; Thierry Lecroq; Ahmed Rebaï
183-197
Discriminant Subspace Learning Based on Support Vectors Machines
Nikolaos Pitelis; Anastasios Tefas
198-212
A New Learning Strategy of General BAMs
Nong Thi Hoa; Bui The Duy
213-221
Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empirical Comparison
Godfried T. Toussaint; Constantin Berzan
222-236
Semi Supervised Clustering: A Pareto Approach
Javid Ebrahimi; Mohammad Saniee Abadeh
237-251
Chapters/HeadingsAuthor(s)PagesInfo
Showing 1 to 20 of 53 entries
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