Léon Bottou

Olivier Chapelle

Dennis Decoste

Jason Weston

Topics covered include fast implementations of known algorithms, approximations that are amenable to theoretical guarantees, and algorithms that perform well in practice but are difficult to analyze theoretically.

Large-scale Kernel Machines

Marvin Minsky

Seymour A. Papert

Reissue of the 1988 Expanded Edition with a new foreword by Léon Bottou In 1969, ten years after the discovery of the perceptron -- which showed that a machine could be taught to perform certain tasks using examples -- Marvin Minsky and Seymour Papert published Perceptrons, their analysis of the computational capabilities of perceptrons for specific tasks. As Léon Bottou writes in his foreword to this edition, "Their rigorous work and brilliant technique does not make the perceptron look very good." Perhaps as a result, research turned away from the perceptron. Then the pendulum swung back, and machine learning became the fastest-growing field in computer science. Minsky and Papert's insistence on its theoretical foundations is newly relevant. Perceptrons -- the first systematic study of parallelism in computation -- marked a historic turn in artificial intelligence, returning to the idea that intelligence might emerge from the activity of networks of neuron-like entities. Minsky and Papert provided mathematical analysis that showed the limitations of a class of computing machines that could be considered as models of the brain. Minsky and Papert added a new chapter in 1987 in which they discuss the state of parallel computers, and note a central theoretical challenge: reaching a deeper understanding of how "objects" or "agents" with individuality can emerge in a network. Progress in this area would link connectionism with what the authors have called "society theories of mind."

An Introduction to Computational Geometry

Perceptrons

Yaʼir Ṿis

Lawrence K. Saul

Yair Weiss

The annual Neural Information Processing Systems (NIPS) conference is the flagshipmeeting on neural computation. It draws a diverse group of attendees -- physicists, neuroscientists,mathematicians, statisticians, and computer scientists. The presentations are interdisciplinary,with contributions in algorithms, learning theory, cognitive science, neuroscience, brain imaging,vision, speech and signal processing, reinforcement learning and control, emerging technologies, andapplications. Only twenty-five percent of the papers submitted are accepted for presentation atNIPS, so the quality is exceptionally high. This volume contains the papers presented at theDecember, 2004 conference, held in Vancouver.

Proceedings of the 2004 Conference

Advances in Neural Information Processing Systems 17

Léon Bottou

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