An Eigendecomposition Approach to Weighted Graph Matching Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Neural Computation
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
On cross validation for model selection
Neural Computation
An Introduction to Variational Methods for Graphical Models
Machine Learning
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Estimation of Dependences Based on Empirical Data: Empirical Inference Science (Information Science and Statistics)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Bayesian Ying Yang system, best harmony learning, and Gaussian manifold based family
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
One-Bit-Matching ICA theorem, convex-concave programming, and combinatorial optimization
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
A PCA approach for fast retrieval of structural patterns inattributed graphs
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
BYY harmony learning, independent state space, and generalized APT financial analyses
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Temporal BYY encoding, Markovian state spaces, and space dimension determination
IEEE Transactions on Neural Networks
Canonical Dual Approach to Binary Factor Analysis
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Bayesian Ying Yang system, best harmony learning, and Gaussian manifold based family
WCCI'08 Proceedings of the 2008 IEEE world conference on Computational intelligence: research frontiers
Review: Supervised classification and mathematical optimization
Computers and Operations Research
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Both optimization and learning play important roles in a system for intelligent tasks. On one hand, we introduce three types of optimization tasks studied in the machine learning literature, corresponding to the three levels of inverse problems in an intelligent system. Also, we discuss three major roles of convexity in machine learning, either directly towards a convex programming or approximately transferring a difficult problem into a tractable one in help of local convexity and convex duality. No doubly, a good optimization algorithm takes an essential role in a learning process and new developments in the literature of optimization may thrust the advances of machine learning. On the other hand, we also interpret that the key task of learning is not simply optimization, as sometimes misunderstood in the optimization literature. We introduce the key challenges of learning and the current status of efforts towards the challenges. Furthermore, learning versus optimization has also been examined from a unified perspective under the name of Bayesian Ying-Yang learning, with combinatorial optimization made more effectively in help of learning.