Elements of information theory
Elements of information theory
Machine Learning
Scale-sensitive dimensions, uniform convergence, and learnability
Journal of the ACM (JACM)
A Unified Framework for Regularization Networks and Support Vector Machines
A Unified Framework for Regularization Networks and Support Vector Machines
Maximum entropy discrimination
Maximum entropy discrimination
Approximate solutions to markov decision processes
Approximate solutions to markov decision processes
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
A Leave-One-out Cross Validation Bound for Kernel Methods with Applications in Learning
COLT '01/EuroCOLT '01 Proceedings of the 14th Annual Conference on Computational Learning Theory and and 5th European Conference on Computational Learning Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Leave-one-out bounds for kernel methods
Neural Computation
Pac-bayesian generalisation error bounds for gaussian process classification
The Journal of Machine Learning Research
Greedy algorithms for classification—consistency, convergence rates, and adaptivity
The Journal of Machine Learning Research
Generalization error bounds for Bayesian mixture algorithms
The Journal of Machine Learning Research
Perturbation to enhance support vector machines for classification
Journal of Computational and Applied Mathematics - Special issue on proceedings of the international symposium on computational mathematics and applications
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Online Choice of Active Learning Algorithms
The Journal of Machine Learning Research
Semi-Supervised Learning on Riemannian Manifolds
Machine Learning
Predicting the disulfide bonding state of cysteines with combinations of kernel machines
Journal of VLSI Signal Processing Systems - Special issue on signal processing and neural networks for bioinformatics
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition
The Journal of Machine Learning Research
Support Vector Machine Soft Margin Classifiers: Error Analysis
The Journal of Machine Learning Research
Stability of Randomized Learning Algorithms
The Journal of Machine Learning Research
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
Neural Computation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Bootstrapping rule induction to achieve rule stability and reduction
Journal of Intelligent Information Systems
Demonstrating the stability of support vector machines for classification
Signal Processing - Signal processing in UWB communications
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-kernel regularized classifiers
Journal of Complexity
Stability of Unstable Learning Algorithms
Machine Learning
Pattern Recognition for Conditionally Independent Data
The Journal of Machine Learning Research
Stability Properties of Empirical Risk Minimization over Donsker Classes
The Journal of Machine Learning Research
Learning random walks to rank nodes in graphs
Proceedings of the 24th international conference on Machine learning
Magnitude-preserving ranking algorithms
Proceedings of the 24th international conference on Machine learning
Learning to Transform Time Series with a Few Examples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Stability of transductive regression algorithms
Proceedings of the 25th international conference on Machine learning
Query-level stability and generalization in learning to rank
Proceedings of the 25th international conference on Machine learning
Estimating the Confidence Interval for Prediction Errors of Support Vector Machine Classifiers
The Journal of Machine Learning Research
Large margin vs. large volume in transductive learning
Machine Learning
Spectral algorithms for supervised learning
Neural Computation
Neighborhood-Based Local Sensitivity
ECML '07 Proceedings of the 18th European conference on Machine Learning
Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCA
ECML '07 Proceedings of the 18th European conference on Machine Learning
ECML '07 Proceedings of the 18th European conference on Machine Learning
How an Ensemble Method Can Compute a Comprehensible Model
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Generalization Bounds for Some Ordinal Regression Algorithms
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Sample Selection Bias Correction Theory
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Estimation of individual prediction reliability using the local sensitivity analysis
Applied Intelligence
Comparison of approaches for estimating reliability of individual regression predictions
Data & Knowledge Engineering
Learning with sample dependent hypothesis spaces
Computers & Mathematics with Applications
Learning rates of gradient descent algorithm for classification
Journal of Computational and Applied Mathematics
Promoting Diversity in Gaussian Mixture Ensembles: An Application to Signature Verification
Biometrics and Identity Management
Patch clustering for massive data sets
Neurocomputing
Transfer bounds for linear feature learning
Machine Learning
An overview of advances in reliability estimation of individual predictions in machine learning
Intelligent Data Analysis
Genetic programming for protein related text classification
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Stability and Performances in Biclustering Algorithms
Computational Intelligence Methods for Bioinformatics and Biostatistics
Quantifying the impact of learning algorithm parameter tuning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Robust support vector machine training via convex outlier ablation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Asymptotic efficiency of kernel support vector machines (SVM)
Cybernetics and Systems Analysis
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Spectrum of variable-random trees
Journal of Artificial Intelligence Research
Transductive Rademacher complexity and its applications
Journal of Artificial Intelligence Research
Boosting and instability for regression trees
Computational Statistics & Data Analysis
Robustness of reweighted Least Squares Kernel Based Regression
Journal of Multivariate Analysis
A collaborative training algorithm for distributed learning
IEEE Transactions on Information Theory
Pattern Recognition Letters
Sentiment Classification with Support Vector Machines and Multiple Kernel Functions
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Effect of Subsampling Rate on Subbagging and Related Ensembles of Stable Classifiers
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Robustness and Regularization of Support Vector Machines
The Journal of Machine Learning Research
Stability Bounds for Stationary φ-mixing and β-mixing Processes
The Journal of Machine Learning Research
An alternative ranking problem for search engines
WEA'07 Proceedings of the 6th international conference on Experimental algorithms
Transductive rademacher complexity and its applications
COLT'07 Proceedings of the 20th annual conference on Learning theory
Support vector regression methods for functional data
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Information Theory
The Knowledge Engineering Review
The Journal of Machine Learning Research
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
Vector field learning via spectral filtering
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Approximation stability and boosting
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
Distribution-dependent PAC-bayes priors
ALT'10 Proceedings of the 21st international conference on Algorithmic learning theory
On complexity issues of online learning algorithms
IEEE Transactions on Information Theory
Learnability, Stability and Uniform Convergence
The Journal of Machine Learning Research
On Convergence of Kernel Learning Estimators
SIAM Journal on Optimization
On qualitative robustness of support vector machines
Journal of Multivariate Analysis
Learning good edit similarities with generalization guarantees
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Almost-everywhere algorithmic stability and generalization error
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
SIAM Journal on Computing
The bounds on the rate of uniform convergence for learning machine
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
The generalization performance of learning machine with NA dependent sequence
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
A sober look at clustering stability
COLT'06 Proceedings of the 19th annual conference on Learning Theory
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Function classes that approximate the bayes risk
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Unifying divergence minimization and statistical inference via convex duality
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Stability and generalization of bipartite ranking algorithms
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Permutation tests for classification
COLT'05 Proceedings of the 18th annual conference on Learning Theory
Training regression ensembles by sequential target correction and resampling
Information Sciences: an International Journal
Local negative correlation with resampling
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A boosting approach for supervised Mahalanobis distance metric learning
Pattern Recognition
Learning Rates for Regularized Classifiers Using Trigonometric Polynomial Kernels
Neural Processing Letters
Robust re-identification using randomness and statistical learning: Quo vadis
Pattern Recognition Letters
Nonlinearity in Forecasting of High-Frequency Stock Returns
Computational Economics
A variance reduction framework for stable feature selection
Statistical Analysis and Data Mining
Machine learning based analysis of gender differences in visual inspection decision making
Information Sciences: an International Journal
Tighter PAC-Bayes bounds through distribution-dependent priors
Theoretical Computer Science
Sentiment analysis of user comments for one-class collaborative filtering over ted talks
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Multiple spectral kernel learning and a gaussian complexity computation
Neural Computation
Differential privacy for functions and functional data
The Journal of Machine Learning Research
On the convergence rate of lp-norm multiple kernel learning
The Journal of Machine Learning Research
Eigenvalues perturbation of integral operator for kernel selection
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Uniform convergence, stability and learnability for ranking problems
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Link label prediction in signed social networks
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Unlabeled patterns to tighten Rademacher complexity error bounds for kernel classifiers
Pattern Recognition Letters
Domain adaptation and sample bias correction theory and algorithm for regression
Theoretical Computer Science
Learning theory analysis for association rules and sequential event prediction
The Journal of Machine Learning Research
Guaranteed classification via regularized similarity learning
Neural Computation
Reconstruction of Transcriptional Regulatory Networks by Stability-Based Network Component Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Generalization Bounds of Regularization Algorithm with Gaussian Kernels
Neural Processing Letters
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We define notions of stability for learning algorithms and show how to use these notions to derive generalization error bounds based on the empirical error and the leave-one-out error. The methods we use can be applied in the regression framework as well as in the classification one when the classifier is obtained by thresholding a real-valued function. We study the stability properties of large classes of learning algorithms such as regularization based algorithms. In particular we focus on Hilbert space regularization and Kullback-Leibler regularization. We demonstrate how to apply the results to SVM for regression and classification.