Algorithmic Learning Theory: 26th International Conference, by Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles

By Kamalika Chaudhuri, CLAUDIO GENTILE, Sandra Zilles

This booklet constitutes the complaints of the twenty sixth overseas convention on Algorithmic studying concept, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th overseas convention on Discovery technology, DS 2015. The 23 complete papers provided during this quantity have been conscientiously reviewed and chosen from forty four submissions. furthermore the publication comprises 2 complete papers summarizing the invited talks and a couple of abstracts of invited talks. The papers are geared up in topical sections named: inductive inference; studying from queries, educating complexity; computational studying thought and algorithms; statistical studying thought and pattern complexity; on-line studying, stochastic optimization; and Kolmogorov complexity, algorithmic info theory.

Show description

Read Online or Download Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings PDF

Similar data mining books

Transactions on Rough Sets XIII

The LNCS magazine Transactions on tough units is dedicated to the complete spectrum of tough units comparable concerns, from logical and mathematical foundations, via all elements of tough set thought and its purposes, akin to info mining, wisdom discovery, and clever info processing, to family members among tough units and different methods to uncertainty, vagueness, and incompleteness, resembling fuzzy units and concept of facts.

Knowledge Discovery Practices and Emerging Applications of Data Mining: Trends and New Domains

Fresh advancements have vastly elevated the quantity and complexity of knowledge on hand to be mined, top researchers to discover new how one can glean non-trivial info immediately. wisdom Discovery Practices and rising purposes of knowledge Mining: developments and New domain names introduces the reader to fresh study actions within the box of knowledge mining.

Requirements Engineering in the Big Data Era: Second Asia Pacific Symposium, APRES 2015, Wuhan, China, October 18–20, 2015, Proceedings

This booklet constitutes the lawsuits of the second one Asia Pacific standards Engineering Symposium, APRES 2015, held in Wuhan, China, in October 2015. The nine complete papers provided including three device demos papers and one brief paper, have been rigorously reviewed and chosen from 18 submissions. The papers care for a number of features of necessities engineering within the colossal information period, reminiscent of computerized necessities research, requisites acquisition through crowdsourcing, requirement tactics and necessities, necessities engineering instruments.

Extra resources for Algorithmic Learning Theory: 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings

Example text

Journal of Symbolic Computation 40(6), 1302–1324 (2005) 31. : Subtracting a best rank-1 approximation may increase tensor rank. Linear Algebra and Its Applications 433, 1276–1300 (2010) 32. : Perturbation bounds in connection with singular value decomposition. BIT Numerical Mathematics 12(1), 99–111 (1972) 33. : Rank-one approximation to high order tensors. sg Abstract. In iterative learning the memory of the learner can only be updated when the hypothesis changes; this results in only finitely many updates of memory during the overall learning history.

6 Experiments In this section, we demonstrate empirically that our Gaussian rank-one linear operators are significantly more efficient for matrix sensing than the existing RIP based measurement operators. In particular, we apply the two recovery methods namely alternating minimization (ALS) and nuclear norm minimization (Nuclear) to the measurements obtained using three different operators: rankone independent (Rank1 Indep), rank-one dependent (Rank1 Dep), and a RIP based operator generated using random Gaussian matrices (RIP).

The problem can then be solved by a variety of approaches, including fixed-point and variational methods. One approach for obtaining the orthogonal decomposition is the tensor power method of [21, Remark3]. We provide a convergence analysis of this method for orthogonally decomposable symmetric tensors, as well as a robust (and computationally tractable) variant. The perturbation analysis in [1] can be viewed as an analogue of Wedin’s perturbation theorem for singular vectors of matrices [32], providing a bound on the error of the recovered decomposition in terms of the operator norm of the tensor perturbation.

Download PDF sample

Rated 4.10 of 5 – based on 9 votes