ECML 2007 - PDF Preprint Papers

ECML 2007 European Working Session on Learning

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ECML
2007
Springer

263views Machine Learning» more ECML 2007»

Avoiding Boosting Overfitting by Removing Confusing Samples

14 years 5 months ago

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Boosting methods are known to exhibit noticeable overfitting on some datasets, while being immune to overfitting on other ones. In this paper we show that standard boosting algorit...

Alexander Vezhnevets, Olga Barinova

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Id	Views	Title	Status

1	263	Avoiding Boosting Overfitting by Removing Confusing Samples
2	226	Bayesian Inference for Sparse Generalized Linear Models	claim
3	207	Dual Strategy Active Learning	claim
4	192	Policy Gradient Critics	claim
5	188	Spectral Clustering and Embedding with Hidden Markov Models	claim
6	187	Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches	claim
7	185	Using Text Mining and Link Analysis for Software Mining	claim
8	184	Efficient Computation of Recursive Principal Component Analysis for Structured Input	claim
9	183	An Unsupervised Learning Algorithm for Rank Aggregation	claim
10	176	Learning Metrics Between Tree Structured Data: Application to Image Recognition	claim
11	175	Principal Component Analysis for Large Scale Problems with Lots of Missing Values	claim
12	171	Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search	claim
13	171	Graph-Based Domain Mapping for Transfer Learning in General Games	claim
14	170	Sequence Labeling with Reinforcement Learning and Ranking Algorithms	claim
15	169	Probabilistic Models for Action-Based Chinese Dependency Parsing	claim
16	168	Approximating Gaussian Processes with H2-Matrices	claim
17	167	Clustering Trees with Instance Level Constraints	claim
18	167	Multi-objective Genetic Programming for Multiple Instance Learning	claim
19	167	Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs	claim
20	164	Seeing the Forest Through the Trees: Learning a Comprehensible Model from an Ensemble	claim
21	161	Statistical Debugging Using Latent Topic Models	claim
22	160	Data Mining of Multi-categorized Data	claim
23	156	Analyzing Co-training Style Algorithms	claim
24	155	Learning an Outlier-Robust Kalman Filter	claim
25	155	Quality of Musical Instrument Sound Identification for Various Levels of Accompanying Sounds	claim
26	154	On Pairwise Naive Bayes Classifiers	claim
27	153	Learning from Relevant Tasks Only	claim
28	151	Decision Tree Instability and Active Learning	claim
29	151	Discovering Word Meanings Based on Frequent Termsets	claim
30	146	Learning Balls of Strings with Correction Queries	claim
31	143	Generalization-Based Similarity for Conceptual Clustering	claim
32	142	Classifier Loss Under Metric Uncertainty	claim
33	141	Weighted Kernel Regression for Predicting Changing Dependencies	claim
34	140	Discriminative Sequence Labeling by Z-Score Optimization	claim
35	140	Additive Groves of Regression Trees	claim
36	138	Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov Models	claim
37	138	Ordinal Classification with Decision Rules	claim
38	137	On Minimizing the Position Error in Label Ranking	claim
39	137	Learning Partially Observable Markov Models from First Passage Times	claim
40	136	Neighborhood-Based Local Sensitivity	claim
41	135	On Phase Transitions in Learning Sparse Networks	claim
42	135	An Improved Model Selection Heuristic for AUC	claim
43	135	Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA	claim
44	135	Source Separation with Gaussian Process Models	claim
45	135	User Oriented Hierarchical Information Organization and Retrieval	claim
46	133	Structure Learning of Probabilistic Relational Models from Incomplete Relational Data	claim
47	133	Imitation Learning Using Graphical Models	claim
48	133	Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling	claim
49	131	Optimizing Feature Sets for Structured Data	claim
50	130	Nondeterministic Discretization of Weights Improves Accuracy of Neural Networks	claim
51	130	Planning and Learning in Environments with Delayed Feedback	claim
52	130	Conceptual Clustering Applied to Ontologies	claim
53	130	Finding Composite Episodes	claim
54	129	Class Noise Mitigation Through Instance Weighting	claim
55	129	Using Secondary Knowledge to Support Decision Tree Classification of Retrospective Clinical Data	claim
56	129	Semi-supervised Collaborative Text Classification	claim
57	128	Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule Learning	claim
58	128	Modeling Highway Traffic Volumes	claim
59	128	Ensembles of Multi-Objective Decision Trees	claim
60	128	Trajectory Analysis of Laboratory Tests as Medical Complex Data Mining	claim
61	126	Learning to Classify Documents with Only a Small Positive Training Set	claim
62	125	Roulette Sampling for Cost-Sensitive Learning	claim
63	119	Probabilistic Explanation Based Learning	claim
64	116	Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators	claim
65	114	Scale-Space Based Weak Regressors for Boosting	claim
66	108	Safe Q-Learning on Complete History Spaces	claim
67	107	A Simple Lexicographic Ranker and Probability Estimator	claim

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