LE T H A; LE H M; DINH T P.Feature selection in machine learning: an exact penalty approach using a difference of convex function algorithm.Machine Learning.2015.163-186Le Thi, H.A., Le, H.M., Pham, D.T.: Featur
I couldn't find this in listed errata online. 2 edited title PRML Errata: Log convex function is actually log concave In Pattern Recognition and Machine Learning Ch 6.4.6 at the bottom of page 316 the author states thatp(aN|tN)p(aN|tN)is log convex. The author states that: −∇...
炼丹魔法书-Convex Optimization for Machine Learning 这本书是由 Michael Nielsen 和 Isaac Schreiber 合著的，于2019年由MIT出版社出版。该书是机器学习领域中关于非凸优化问题的经典著作之一，主要介绍了一些非凸优化算法以及如何求解非凸优化问题。书中主要讲了两种非凸情况：一是目标函数是凸的，约束集合不是凸的...
However, if you cross the function line, then the function is non-convex. A non-convex function As you can see in the figure above, the red line crosses the function, which means it is non-convex. Note, however, that the function is convex on some intervals, for instance on [-1,+1...
We propose a combination of machine learning and flux limiting for property-preserving subgrid scale modeling in the context of flux-limited finite volume methods for the one-dimensional shallow-water equations. The numerical fluxes of a conservative target scheme are fitted to the coarse-mesh ...
2020,Machine Learning (Second Edition) Review article Multi-objective optimization for spectrum sharing in cognitive radio networks: A review 4.5Convex optimization A power control problem inCRNsis formulated as anMOO problem, through developing a convexcost function, based onSINRand transmit power of ...
These methods might be useful in the core of your own implementation of a machine learning algorithm. You may want to implement your own algorithm tuning scheme to optimize the parameters of a model for some cost function. A good example may be the case where you want to optimize the hyper...
Differentiable Quasiconvex Function Strictly Quasiconvex Function Strongly Quasiconvex Function Pseudoconvex Function Convex Programming Problem Fritz-John Conditions Karush-Kuhn-Tucker Optimality Necessary Conditions Algorithms for Convex Problems Convex Optimization - Quick Guide Convex Optimization - Resources Co...
Many problems in machine learning and other fields can be (re)formulated as linearly constrained separable convex programs. In most of the cases, there are
layer feedforward networks (SLFNs) with randomly generated additive or radial basis function (RBF) hidden nodes (according to any continuous sampling distribution) can work as universal approximators and the resulting incremental extreme learning machine (I-ELM) outperforms many popular learning ...