← Learning Types
Machine Learning › Learning Types ›

Classification

1251 directly classified papers

Papers per year

Papers

ShareBoost: Efficient multiclass learning with feature sharing NIPS 2011
Robust Multi-Class Gaussian Process Classification NIPS 2011
Learning large-margin halfspaces with more malicious noise NIPS 2011
Selective Prediction of Financial Trends with Hidden Markov Models NIPS 2011
Multiclass Learnability and the ERM principle COLT 2011
Similarity-based Learning via Data Driven Embeddings NIPS 2011
Unsupervised Supervised Learning II: Margin-Based Classification without Labels AISTATS 2011
Neyman-Pearson classification under a strict constraint COLT 2011
The Rate of Convergence of Adaboost COLT 2011
Sample Complexity Bounds for Differentially Private Learning COLT 2011
Joint Cascade Optimization Using A Product Of Boosted Classifiers NIPS 2010
Multitask Learning without Label Correspondences NIPS 2010
Avoiding False Positive in Multi-Instance Learning NIPS 2010
Agnostic Active Learning Without Constraints NIPS 2010
Relaxed Clipping: A Global Training Method for Robust Regression and Classification NIPS 2010
On Herding and the Perceptron Cycling Theorem NIPS 2010
Empirical Bernstein Boosting AISTATS 2010
Large Margin Multi-Task Metric Learning NIPS 2010
Boosting Classifier Cascades NIPS 2010
Large Margin Learning of Upstream Scene Understanding Models NIPS 2010
Tight Sample Complexity of Large-Margin Learning NIPS 2010
Online Classification with Specificity Constraints NIPS 2010
Sparsistent Learning of Varying-coefficient Models with Structural Changes NIPS 2009
Distribution-Calibrated Hierarchical Classification NIPS 2009
Free energy score space NIPS 2009