Download e-book for kindle: Advances in Neural Information Processing Systems 19: by Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann

Download e-book for kindle: Advances in Neural Information Processing Systems 19: by Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann

By Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann (ed.)

ISBN-10: 0262195682

ISBN-13: 9780262195683

ISBN-10: 0262256916

ISBN-13: 9780262256919

The yearly Neural info Processing structures (NIPS) convention is the flagship assembly on neural computation and computing device studying. It attracts a various staff of attendees—physicists, neuroscientists, mathematicians, statisticians, and computing device scientists—interested in theoretical and utilized points of modeling, simulating, and development neural-like or clever platforms. The displays are interdisciplinary, with contributions in algorithms, studying conception, cognitive technology, neuroscience, mind imaging, imaginative and prescient, speech and sign processing, reinforcement studying, and purposes. in basic terms twenty-five percentage of the papers submitted are authorised for presentation at NIPS, so the standard is phenomenally excessive. This quantity includes the papers offered on the December 2006 assembly, held in Vancouver.

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Extra info for Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference

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We ran the experiments on the sequence of email messages from 7 different users. Since each user employs different criteria for email classification, we treated each person as a separate online learning problem. We represented each email message as a vector with a component for every word in the corpus. On each trial, and for each class r, we constructed class-dependent vectors as follows. We set φj (xt , r) to twice the number of time the j’th word appeared in the message if it had also appeared in a fifth of the messages previously assigned to folder r.

We then introduce a general method for approximately solving the problem by projecting simultaneously and independently on each constraint which corresponds to a prediction sub-problem, and then averaging the individual solutions. We show that this approach constitutes a feasible, albeit not necessarily optimal, solution for the original projection problem. We derive concrete simultaneous projection schemes and analyze them in the mistake bound model. We demonstrate the power of the proposed algorithm in experiments with online multiclass text categorization.

We project on each halfspace in parallel and the new vector is a weighted average of these projections Ordinal Regression In the problem of ordinal regression an instance x is a vector of n features that is associated with a target rank y ∈ [k]. A learning algorithm is required to find a vector ω and k thresholds b1 ≤ · · · ≤ bk−1 ≤ bk = ∞. The value of ω · x provides a score from which the prediction value can be defined as the smallest index i for which ω · x < bi , yˆ = min {i|ω · x < bi }. In order to obtain a correct prediction, an ordinal regressor is required to ensure that ω ·x ≥ bi for all i < y and that ω · x < bi for i ≥ y.

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Advances in Neural Information Processing Systems 19: Proceedings of the 2006 Conference by Bernhard Schölkopf (ed.), John Platt (ed.), Thomas Hofmann (ed.)


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