A Fast Dual Algorithm for Kernel Logistic Regression

Authors:
S. S. Keerthi;K. B. Duan;S. K. Shevade;A. N. Poo
Affiliations:
Yahoo! Research Labs, Pasadena, USA 91105;Control Division, Department of Mechanical Engineering, National University of Singapore, Singapore;Department of Computer Science and Automation, Indian Institute of Science, Bangalore, India;Control Division, Department of Mechanical Engineering, National University of Singapore, Singapore
Venue:
Machine Learning
Year:
2005

Citing 3
Cited 16

The nature of statistical learning theory

The nature of statistical learning theory
Bayesian Classification With Gaussian Processes

IEEE Transactions on Pattern Analysis and Machine Intelligence
Improvements to Platt's SMO Algorithm for SVM Classifier Design

Neural Computation

Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters

The Journal of Machine Learning Research
Exponentiated gradient algorithms for log-linear structured prediction

Proceedings of the 24th international conference on Machine learning
Sparse probabilistic classifiers

Proceedings of the 24th international conference on Machine learning
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks

The Journal of Machine Learning Research
Large-scale sparse logistic regression

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models

The Journal of Machine Learning Research
Sparse approximation through boosting for learning large scale kernel machines

IEEE Transactions on Neural Networks
CAFE: Collaboration Aimed at Finding Experts

International Journal of Knowledge and Web Intelligence
Regularized logistic regression without a penalty term: An application to cancer classification with microarray data

Expert Systems with Applications: An International Journal
Classifying dialogue in high-dimensional space

ACM Transactions on Speech and Language Processing (TSLP)
Editors Choice Article: I2VM: Incremental import vector machines

Image and Vision Computing
Supervised learning algorithms for multi-class classification problems with partial class memberships

Fuzzy Sets and Systems
Accurate Prediction of Coronary Artery Disease Using Reliable Diagnosis System

Journal of Medical Systems
Density-based logistic regression

Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic construction of anomaly detection benchmarks from real data

Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
Regularized vector field learning with sparse approximation for mismatch removal

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper gives a new iterative algorithm for kernel logistic regression. It is based on the solution of a dual problem using ideas similar to those of the Sequential Minimal Optimization algorithm for Support Vector Machines. Asymptotic convergence of the algorithm is proved. Computational experiments show that the algorithm is robust and fast. The algorithmic ideas can also be used to give a fast dual algorithm for solving the optimization problem arising in the inner loop of Gaussian Process classifiers.