Beyond the Worst-Case Analysis of Algorithms PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Beyond the Worst-Case Analysis of Algorithms PDF full book. Access full book title Beyond the Worst-Case Analysis of Algorithms by Tim Roughgarden. Download full books in PDF and EPUB format.
Author: Tim Roughgarden
Publisher: Cambridge University Press
ISBN: 1108786170
Category : Computers
Languages : en
Pages : 705
Get Book Here
Book Description
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Author: Tim Roughgarden
Publisher: Cambridge University Press
ISBN: 1108786170
Category : Computers
Languages : en
Pages : 705
Get Book Here
Book Description
There are no silver bullets in algorithm design, and no single algorithmic idea is powerful and flexible enough to solve every computational problem. Nor are there silver bullets in algorithm analysis, as the most enlightening method for analyzing an algorithm often depends on the problem and the application. However, typical algorithms courses rely almost entirely on a single analysis framework, that of worst-case analysis, wherein an algorithm is assessed by its worst performance on any input of a given size. The purpose of this book is to popularize several alternatives to worst-case analysis and their most notable algorithmic applications, from clustering to linear programming to neural network training. Forty leading researchers have contributed introductions to different facets of this field, emphasizing the most important models and results, many of which can be taught in lectures to beginning graduate students in theoretical computer science and machine learning.
Author: Ali H. Sayed
Publisher: Cambridge University Press
ISBN: 100921828X
Category : Computers
Languages : en
Pages : 1081
Get Book Here
Book Description
Discover data-driven learning methods with the third volume of this extraordinary three-volume set.
Author: George H. Chen
Publisher: Foundations and Trends (R) in Machine Learning
ISBN: 9781680834543
Category :
Languages : en
Pages : 264
Get Book Here
Book Description
Explains the success of Nearest Neighbor Methods in Prediction, both in theory and in practice.
Author: Lior Rokach
Publisher: Springer Nature
ISBN: 3031246284
Category : Computers
Languages : en
Pages : 975
Get Book Here
Book Description
This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.
Author: Antonio Ortega
Publisher: Cambridge University Press
ISBN: 1108428134
Category : Computers
Languages : en
Pages : 321
Get Book Here
Book Description
An intuitive, accessible text explaining the fundamentals and applications of signal processing on graphs. It covers basic and advanced topics, includes numerous exercises and Matlab examples, and is accompanied online by a solutions manual for instructors, making it essential reading for graduate students, researchers, and industry professionals.
Author: Ioannis N. Parasidis
Publisher: Springer Nature
ISBN: 3030847217
Category : Mathematics
Languages : en
Pages : 1050
Get Book Here
Book Description
This contributed volume provides an extensive account of research and expository papers in a broad domain of mathematical analysis and its various applications to a multitude of fields. Presenting the state-of-the-art knowledge in a wide range of topics, the book will be useful to graduate students and researchers in theoretical and applicable interdisciplinary research. The focus is on several subjects including: optimal control problems, optimal maintenance of communication networks, optimal emergency evacuation with uncertainty, cooperative and noncooperative partial differential systems, variational inequalities and general equilibrium models, anisotropic elasticity and harmonic functions, nonlinear stochastic differential equations, operator equations, max-product operators of Kantorovich type, perturbations of operators, integral operators, dynamical systems involving maximal monotone operators, the three-body problem, deceptive systems, hyperbolic equations, strongly generalized preinvex functions, Dirichlet characters, probability distribution functions, applied statistics, integral inequalities, generalized convexity, global hyperbolicity of spacetimes, Douglas-Rachford methods, fixed point problems, the general Rodrigues problem, Banach algebras, affine group, Gibbs semigroup, relator spaces, sparse data representation, Meier-Keeler sequential contractions, hybrid contractions, and polynomial equations. Some of the works published within this volume provide as well guidelines for further research and proposals for new directions and open problems.
Author: Francis Bach
Publisher: MIT Press
ISBN: 0262049449
Category : Computers
Languages : en
Pages : 497
Get Book Here
Book Description
A comprehensive and cutting-edge introduction to the foundations and modern applications of learning theory. Research has exploded in the field of machine learning resulting in complex mathematical arguments that are hard to grasp for new comers. . In this accessible textbook, Francis Bach presents the foundations and latest advances of learning theory for graduate students as well as researchers who want to acquire a basic mathematical understanding of the most widely used machine learning architectures. Taking the position that learning theory does not exist outside of algorithms that can be run in practice, this book focuses on the theoretical analysis of learning algorithms as it relates to their practical performance. Bach provides the simplest formulations that can be derived from first principles, constructing mathematically rigorous results and proofs without overwhelming students. Provides a balanced and unified treatment of most prevalent machine learning methods Emphasizes practical application and features only commonly used algorithmic frameworks Covers modern topics not found in existing texts, such as overparameterized models and structured prediction Integrates coverage of statistical theory, optimization theory, and approximation theory Focuses on adaptivity, allowing distinctions between various learning techniques Hands-on experiments, illustrative examples, and accompanying code link theoretical guarantees to practical behaviors
Author: Hayit Greenspan
Publisher: Springer Nature
ISBN: 3031439996
Category : Computers
Languages : en
Pages : 832
Get Book Here
Book Description
The ten-volume set LNCS 14220, 14221, 14222, 14223, 14224, 14225, 14226, 14227, 14228, and 14229 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada, in October 2023. The 730 revised full papers presented were carefully reviewed and selected from a total of 2250 submissions. The papers are organized in the following topical sections: Part I: Machine learning with limited supervision and machine learning – transfer learning; Part II: Machine learning – learning strategies; machine learning – explainability, bias, and uncertainty; Part III: Machine learning – explainability, bias and uncertainty; image segmentation; Part IV: Image segmentation; Part V: Computer-aided diagnosis; Part VI: Computer-aided diagnosis; computational pathology; Part VII: Clinical applications – abdomen; clinical applications – breast; clinical applications – cardiac; clinical applications – dermatology; clinical applications – fetal imaging; clinical applications – lung; clinical applications – musculoskeletal; clinical applications – oncology; clinical applications – ophthalmology; clinical applications – vascular; Part VIII: Clinical applications – neuroimaging; microscopy; Part IX: Image-guided intervention, surgical planning, and data science; Part X: Image reconstruction and image registration.
Author: Frank Hutter
Publisher: Springer Nature
ISBN: 3030676617
Category : Computers
Languages : en
Pages : 770
Get Book Here
Book Description
The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.
Author: Anil Ananthaswamy
Publisher: Penguin
ISBN: 0593185749
Category : Computers
Languages : en
Pages : 481
Get Book Here
Book Description
A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene. We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both? As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.
