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Learning with Nested Generalized Exemplars
Machine Learning is one of the oldest and most intriguing areas of Ar tificial Intelligence. From the moment that computer visionaries first began to conceive the potential for general-purpose symbolic computa tion, the concept of a machine that could learn by itself has been an ever present goal. Today, although there have been many implemented com puter programs that can be said to learn, we are still far from achieving the lofty visions of self-organizing automata that spring to mind when we think of machine learning. We have established some base camps and scaled some of the foothills of this epic intellectual adventure, but we are still far from the lofty peaks that the imagination conj...
Algorithms and Computation
This book constitutes the refereed proceedings of the 14th International Symposium on Algorithms and Computation, ISAAC 2003, held in Kyoto, Japan, in December 2003. The 73 revised full papers presented were carefully reviewed and selected from 207 submissions. The papers are organized in topical sections on computational geometry, graph and combinatorial algorithms, computational complexity, quantum computing, combinatorial optimization, scheduling, computational biology, distributed and parallel algorithms, data structures, combinatorial and network optimization, computational complexity and cryptography, game theory and randomized algorithms, and algebraic and arithmetic computation.
Large-scale Kernel Machines
Solutions for learning from large scale datasets, including kernel learning algorithms that scale linearly with the volume of the data and experiments carried out on realistically large datasets. Pervasive and networked computers have dramatically reduced the cost of collecting and distributing large datasets. In this context, machine learning algorithms that scale poorly could simply become irrelevant. We need learning algorithms that scale linearly with the volume of the data while maintaining enough statistical efficiency to outperform algorithms that simply process a random subset of the data. This volume offers researchers and engineers practical solutions for learning from large scale ...
Using The Biological Literature
"Provides an in-depth review of current print and electronic tools for research in numerous disciplines of biology, including dictionaries and encyclopedias, method guides, handbooks, on-line directories, and periodicals. Directs readers to an associated Web page that maintains the URLs and annotations of all major Inernet resources discussed in th
A Lab of One's Own
Colwell, the first female director of the National Science Foundation, discusses the entrenched sexism in science, the elaborate detours women have taken to bypass the problem, and how to fix the system. When she first applied for a graduate fellowship in bacteriology, she was told, "We don't waste fellowships on women." Over her six decades in science, as she encounters other women pushing back against the status quo, Colwell also witnessed the advances that could be made when men and women worked together. Here she offers an astute diagnosis of how to fix the problem of sexism in science-- and a celebration of the women pushing back.--
Selecting Models from Data
This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. These biennial workshops have succeeded in bringing together researchers from Artificial Intelligence and from Statistics to discuss problems of mutual interest. The exchange has broadened research in both fields and has strongly encour aged interdisciplinary work. The theme ofthe 1993 AI and Statistics workshop was: "Selecting Models from Data". The papers in this volume attest to the diversity of approaches to model selection and to the ubiquity of the problem. Both statistics and artificial intelligence have independently developed approaches to model selection and the corresponding algorithms to implement them. But as these papers make clear, there is a high degree of overlap between the different approaches. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data; in other words, the model has partly fitted the "noise" in the original data.
Probabilistic Data Structures and Algorithms for Big Data Applications
A technical book about popular space-efficient data structures and fast algorithms that are extremely useful in modern Big Data applications. The purpose of this book is to introduce technology practitioners, including software architects and developers, as well as technology decision makers to probabilistic data structures and algorithms. Reading this book, you will get a theoretical and practical understanding of probabilistic data structures and learn about their common uses.
Writing Guide with Handbook
Writing Guide with Handbook aligns to the goals, topics, and objectives of many first-year writing and composition courses. It is organized according to relevant genres, and focuses on the writing process, effective writing practices or strategies—including graphic organizers, writing frames, and word banks to support visual learning—and conventions of usage and style. The text includes an editing and documentation handbook, which provides information on grammar and mechanics, common usage errors, and citation styles. Writing Guide with Handbook breaks down barriers in the field of composition by offering an inviting and inclusive approach to students of all intersectional identities. To...
Genome-Scale Algorithm Design
Presenting the fundamental algorithms and data structures that power bioinformatics workflows, this book covers a range of topics from the foundations of sequence analysis (alignments and hidden Markov models) to classical index structures (k-mer indexes, suffix arrays, and suffix trees), Burrows–Wheeler indexes, graph algorithms, network flows, and a number of advanced omics applications. The chapters feature numerous examples, algorithm visualizations, and exercises, providing graduate students, researchers, and practitioners with a powerful algorithmic toolkit for the applications of high-throughput sequencing. An accompanying website (www.genome-scale.info) offers supporting teaching material. The second edition strengthens the toolkit by covering minimizers and other advanced data structures and their use in emerging pangenomics approaches.
Machine Learning: ECML 2003
This book constitutes the refereed proceedings of the 14th European Conference on Machine Learning, ECML 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with PKDD 2003. The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for PKDD 2003, selected from a total of 332 submissions. The papers address all current issues in machine learning including support vector machine, inductive inference, feature selection algorithms, reinforcement learning, preference learning, probabilistic grammatical inference, decision tree learning, clustering, classification, agent learning, Markov networks, boosting, statistical parsing, Bayesian learning, supervised learning, and multi-instance learning.
