Information Processing Letters
Advances in neural information processing systems 2
Machine Learning
The nature of statistical learning theory
The nature of statistical learning theory
Total Least Norm Formulation and Solution for Structured Problems
SIAM Journal on Matrix Analysis and Applications
Fundamentals of Artificial Neural Networks
Fundamentals of Artificial Neural Networks
Mathematics of Generalization: Proceedings: SFI-CNLS Workshop on Formal Approaches to Supervised Learning (1992: Santa Fe, N. M.)
Discrete Random Signals and Statistical Signal Processing
Discrete Random Signals and Statistical Signal Processing
Readings in Machine Learning
Least Square Estimation with Applications to Digital Signal Processing
Least Square Estimation with Applications to Digital Signal Processing
Mathematical Programming in Data Mining
Data Mining and Knowledge Discovery
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Formulation and solution of structured total least norm problemsfor parameter estimation
IEEE Transactions on Signal Processing
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part II
Data selection for support vector machine classifiers
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization methods in massive data sets
Handbook of massive data sets
The Journal of Machine Learning Research
Dimensionality reduction via sparse support vector machines
The Journal of Machine Learning Research
The disputed federalist papers: SVM feature selection via concave minimization
Proceedings of the 2003 conference on Diversity in computing
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We consider the class of incremental gradient methods for minimizing a sum of continuously differentiable functions. An importantnovel feature of our analysis is that the stepsizes are kept bounded awayfrom zero. We derive the first convergence ...