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Learning Theory
This book constitutes the refereed proceedings of the 17th Annual Conference on Learning Theory, COLT 2004, held in Banff, Canada in July 2004. The 46 revised full papers presented were carefully reviewed and selected from a total of 113 submissions. The papers are organized in topical sections on economics and game theory, online learning, inductive inference, probabilistic models, Boolean function learning, empirical processes, MDL, generalisation, clustering and distributed learning, boosting, kernels and probabilities, kernels and kernel matrices, and open problems.
Machine Learning
Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discri...
Learning Theory and Kernel Machines
This book constitutes the joint refereed proceedings of the 16th Annual Conference on Computational Learning Theory, COLT 2003, and the 7th Kernel Workshop, Kernel 2003, held in Washington, DC in August 2003. The 47 revised full papers presented together with 5 invited contributions and 8 open problem statements were carefully reviewed and selected from 92 submissions. The papers are organized in topical sections on kernel machines, statistical learning theory, online learning, other approaches, and inductive inference learning.
Computer Vision Systems
Computer Vision has now reached a level of maturity that allows us not only to perform research on individual methods but also to build fully integrated computer vision systems of a signi cant complexity. This opens up a number of new problems related to architectures, systems integration, validation of - stems using benchmarking techniques, and so on. So far, the majority of vision conferences have focused on component technologies, which has motivated the organization of the First International Conference on Computer Vision Systems (ICVS). It is our hope that the conference will allow us not only to see a number of interesting new vision techniques and systems but hopefully also to de ne t...
Tractability
Classical computer science textbooks tell us that some problems are 'hard'. Yet many areas, from machine learning and computer vision to theorem proving and software verification, have defined their own set of tools for effectively solving complex problems. Tractability provides an overview of these different techniques, and of the fundamental concepts and properties used to tame intractability. This book will help you understand what to do when facing a hard computational problem. Can the problem be modelled by convex, or submodular functions? Will the instances arising in practice be of low treewidth, or exhibit another specific graph structure that makes them easy? Is it acceptable to use scalable, but approximate algorithms? A wide range of approaches is presented through self-contained chapters written by authoritative researchers on each topic. As a reference on a core problem in computer science, this book will appeal to theoreticians and practitioners alike.
Living with Algorithms
A nuanced account from a user perspective of what it’s like to live in a datafied world. We live in a media-saturated society that increasingly transforms our experiences, relations, and identities into data others can analyze and monetize. Algorithms are key to this process, surveilling our most mundane practices, and to many, their control over our lives seems absolute. In Living with Algorithms, Ignacio Siles critically challenges this view by surveying user dynamics in the global south across three algorithmic platforms—Netflix, Spotify, and TikTok—and finds, surprisingly, a more balanced relationship. Drawing on a wealth of empirical evidence that privileges the user over the corp...
Medicine Meets Virtual Reality 13
Magical describes conditions that are outside our understanding of cause and effect. What cannot be attributed to human or natural forces is explained as magic: super-human, super-natural. Even in modern societies, magic-based explanations are powerful because, given the complexity of the universe, there are so many opportunities to use them. The history of medicine is defined by progress in understanding the human body - from magical explanations to measurable results. To continue medical progress, physicians and scientists must openly question traditional models. Valid inquiry demands a willingness to consider all possible solutions without prejudice. Medical politics should not perpetuate...
The Network Is Your Customer
"An incredibly useful and valuable guidebook to the new consumer economy. Buy it. Learn from it. Succeed with it."--Jeff Jarvis, author of "What Would Google Do " "This is the stuff that every business and nonprofit needs to embrace if they're going to succeed in a changing world."--Vivian Schiller, CEO of NPR With clear analysis and practical frameworks, this book provides a strategic guide that any business or nonprofit can use to succeed in the digital age. Marketing expert David Rogers examines how digital technologies--from smartphones to social networks--connect us in frameworks that transform our relationships to business and each other. To thrive today, organizations need new strateg...
Biometric Authentication
Biometric authentication refers to identifying an individual based on his or her distinguishing physiological and/or behavioral characteristics. It associates an individual with a previously determined identity based on that individual s appearance or behavior. Because many physiological or behavioral characteristics (biometric indicators) are distinctive to each person, biometric identifiers are inherently more reliable and more capable than knowledge-based (e.g., password) and token-based (e.g., a key) techniques in differentiating between an authorized person and a fraudulent impostor. For this reason, more and more organizations are looking to automated identity authentication systems to...
