The nature of statistical learning theory
The nature of statistical learning theory
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Comparing the Bayes and Typicalness Frameworks
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Inductive Confidence Machines for Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Ridge Regression Confidence Machine
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Recognition and Density Estimation under the General i.i.d. Assumption
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
Inductive Confidence Machines for Regression
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Detecting outliers using transduction and statistical testing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Quality assessment of individual classifications in machine learning and data mining
Knowledge and Information Systems
Pattern Recognition Letters
Network anomaly detection based on TCM-KNN algorithm
ASIACCS '07 Proceedings of the 2nd ACM symposium on Information, computer and communications security
Credible Case-Based Inference Using Similarity Profiles
IEEE Transactions on Knowledge and Data Engineering
An anomaly intrusion detection method using the CSI-KNN algorithm
Proceedings of the 2008 ACM symposium on Applied computing
TCM-KNN scheme for network anomaly detection using feature-based optimizations
Proceedings of the 2008 ACM symposium on Applied computing
A Novel Data Mining Method for Network Anomaly Detection Based on Transductive Scheme
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Hedged predictions for traditional Chinese chronic gastritis diagnosis with confidence machine
Computers in Biology and Medicine
Meta-Typicalness Approach to Reliable Classification
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Meta-conformity approach to reliable classification
Intelligent Data Analysis
Normalized nonconformity measures for regression Conformal Prediction
AIA '08 Proceedings of the 26th IASTED International Conference on Artificial Intelligence and Applications
TCM-KNN algorithm for supervised network intrusion detection
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Transductive reliability estimation for kernel based classifiers
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Optimizing network anomaly detection scheme using instance selection mechanism
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Single-stacking conformity approach to reliable classification
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Topology preserving SOM with transductive confidence machine
DS'10 Proceedings of the 13th international conference on Discovery science
Regression conformal prediction with nearest neighbours
Journal of Artificial Intelligence Research
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
A local tangent space alignment based transductive classification algorithm
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Reliable probabilistic classification with neural networks
Neurocomputing
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We propose a new algorithm for pattern recognition that outputs some measures of "reliability" for every prediction made, in contrast to the current algorithms that output "bare" predictions only. Our method uses a rule similar to that of nearest neighbours to infer predictions; thus its predictive performance is close to that of nearest neighbours, while the measures of confidence it outputs provide practically useful information for individual predictions.