Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature...
It continues to be a fundamental paradigm today, with new algorithms being proposed for difficult variants, especially large-scale and nonlinear variants. Thus, SVMs offer excellent common ground on which to demonstrate the interplay of optimization and machine learning. 1.1 Support Vector ...
will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning...
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is ...
In this chapter, we consider two optimization problems - considered to be most central to machine learning and data mining algorithms design - the PCA and LDA computation. We discuss solving these problems in exact way over the cloud environment. We also present these computations when the data ...
【4.2.2】算法优化(Optimization Algorithms) 一、Mini-batch 梯度下降(Mini-batch gradient descent) 本周将学习优化算法,这能让你的神经网络运行得更快。机器学习的应用是一个高度依赖经验的过程,伴随着大量迭代的过程,你需要训练诸多模型,才能找到合适的那一个,所以,优化算法能够帮助你快速训练模型。
The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is ...
Efficient optimization algorithms for machine learning, non-generative unsupervised and semi-supervised learning, online convex optimization and regret minimization in games. 早在2016年,Elad Hazan 就发布了《在线凸优化导论》的第一版: Elad Hazan (2016), "Introduction to Online Convex Optimization", Founda...
First-order Algorithms & Modern Nonconvex Nondifferentiable Optimization **First-order Methods in Optimization by Amir Beck 评注:印象中Amir Beck这本书上过SIAM的推荐,很早之前就想读一下但一直找不到时间。其中FISTA算法更是十年内的重大成果。 First-order and Stochastic Optimization Methods for Machine Lea...
Springer Series in the Data Sciences(共11册),这套丛书还有 《Mathematical Foundations for Data Analysis》《Statistical Inference and Machine Learning for Big Data》《Lectures on the Nearest Neighbor Method》《Cohesive Subgraph Computation over Large Sparse Graphs: Algorithms, Data Structures, and Programmi...