检索结果-内蒙古大学图书馆

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

学校读者我要写书评

暂无评论

Lecture Notes in Computer Science (including subseries Lectu...

Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2013年 8202 LNAI卷 V-VI页

作者： Sun, Maosong Liu, Ting Sun, Le Zhang, Min Lin, Dekang Wang, Haifeng Tsinghua University Department of Computer Science and Technology Beijing China Harbin Institute of Technology China Institute of Software Chinese Academy of Sciences China Soochow University School of Computer Science and Technology Suzhou China Google Inc. Mountain View CA United States Baidu Inc. Beijing China

Reports of the 2013 AAAI Spring Symposium Series

学校读者我要写书评

暂无评论

Reports of the 2013 AAAI Spring Symposium Series

作者： Markman, Vita Stojanov, Georgi Indurkhya, Bipin Kido, Takashi Takadama, Keiki Konidaris, George Eaton, Eric Matsumura, Naohiro Fruchter, Renate Sofge, Don Lawless, William F. Madani, Omid Sukthankar, Rahul Disney Interactive Studios United States American University of Paris France International Institute of Information Technology Hyderabad India Rikengenesis Japan University of Electro-Communications Japan MIT CSAIL United States Computer Science Department Bryn Mawr College United States Graduate School of Economics Osaka University Japan Project Based Learning Laboratory Stanford University United States Naval Research Laboratory Washington DC United States Department of Mathematics and Psychology Paine College Augusta GA United States Google United States

The Association for the Advancement of Artificial Intelligence was pleased to present the AAAI 2013 Spring Symposium Series, held Monday through Wednesday, March 25-27, 2013. The titles of the eight symposia were Analyzing Microtext;Creativity and (Early) Cognitive Development;Data-Driven Wellness: From Self-Tracking to Behavior Change;Designing Intelligent Robots: Reintegrating AI II;Lifelong Machine Learning;Shikakeology: Designing Triggers for Behavior Change;Trust and Autonomous Systems;and Weakly Supervised Learning from Multimedia. This report contains summaries of the symposia, written, in most cases, by the cochairs of the symposium. Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Intelligent robots

Error modeling and estimation fusion for indoor localization

学校读者我要写书评

暂无评论

Error modeling and estimation fusion for indoor localization

2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012

作者： Zhuo, Weipeng Zhang, Bo Chan, S.-H. Gary Chang, Edward Y. Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Google Research Beijing China

ISBN: (纸本)9781467316590

There has been much interest in offering multimedia location-based service (LBS) to indoor users (e.g., sending video/audio streams according to user locations). Offering good LBS largely depends on accurate indoor localization of mobile stations (MSs). To achieve that, in this paper we first model and analyze the error characteristics of important indoor localization schemes, using Radio Frequency Identification (RFID) and Wi-Fi. Our models are simple to use, capturing important system parameters and measurement noises, and quantifying how they affect the accuracies of the localization. Given that there have been many indoor localization techniques deployed, an MS may receive simultaneously multiple co-existing estimations on its location. Equipped with the understanding of location errors, we then investigate how to optimally combine, or fuse, all the co-existing estimations of an MS's location. We present computationally-efficient closed-form expressions to fuse the outputs of the estimators. Simulation and experimental results show that our fusion technique achieves higher location accuracy in spite of location errors in the estimators. © 2012 IEEE.

关键词： Location based services

Lovász ϑ function, SVMs and finding dense subgraphs

学校读者我要写书评

暂无评论

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Vinay Jethava Anders Martinsson Chiranjib Bhattacharyya Devdatt Dubhashi Google MPI for Intelligent Systems Computer Science & Engineering Department Chalmers University of Technology Göteborg Sweden Department of Computer Science and Automation Indian Institute of Science Bangalore Karnataka India

In this paper we establish that the Lovász ϑ function on a graph can be restated as a kernel learning problem. We introduce the notion of SVM-ϑ graphs, on which Lovász ϑ function can be approximated well by a Support vector machine (SVM). We show that Erdös-Rényi random G(n, p) graphs are SVM-ϑ graphs for log4 n/n ≤ p < 1. Even if we embed a large clique of size Θ(√np/1-p) in a G(n, p) graph the resultant graph still remains a SVM-ϑ graph. This immediately suggests an SVM based algorithm for recovering a large planted clique in random graphs. Associated with the ϑ function is the notion of orthogonal labellings. We introduce common orthogonal labellings which extends the idea of orthogonal labellings to multiple graphs. This allows us to propose a Multiple Kernel learning (MKL) based solution which is capable of identifying a large common dense subgraph in multiple graphs. Both in the planted clique case and common subgraph detection problem the proposed solutions beat the state of the art by an order of magnitude.

关键词： common dense subgraph orthogonal labellings of graphs planted cliques random graphs

Bayesian Canonical correlation analysis

学校读者我要写书评

暂无评论

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Arto Klami Seppo Virtanen Samuel Kaski Google MPI for Intelligent Systems Helsinki Institute for Information Technology Department of Information and Computer Science Aalto University Aalto Finland

Canonical correlation analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. We review recent developments in Bayesian models and inference methods for CCA which are attractive for their potential in hierarchical extensions and for coping with the combination of large dimensionalities and small sample sizes. The existing methods have not been particularly successful in fulfilling the promise yet; we introduce a novel efficient solution that imposes group-wise sparsity to estimate the posterior of an extended model which not only extracts the statistical dependencies (correlations) between data sets but also decomposes the data into shared and data set-specific components. In statistics literature the model is known as inter-battery factor analysis (IBFA), for which we now provide a Bayesian treatment.

关键词： Bayesian modeling canonical correlation analysis group-wise sparsity inter-battery factor analysis variational Bayesian approximation

Finding optimal Bayesian networks using precedence constraints

学校读者我要写书评

暂无评论

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Pekka Parviainen Mikko Koivisto Google MPI for Intelligent Systems Science for Life Laboratory School of Computer Science and Communication Royal Institute of Technology (KTH) Solna Sweden Helsinki Institute for Information Technology Department of Computer Science University of Helsinki Helsinki Finland

We consider the problem of finding a directed acyclic graph (DAG) that optimizes a decomposable Bayesian network score. While in a favorable case an optimal DAG can be found in polynomial time, in the worst case the fastest known algorithms rely on dynamic programming across the node subsets, taking time and space 2n, to within a factor polynomial in the number of nodes n. In practice, these algorithms are feasible to networks of at most around 30 nodes, mainly due to the large space requirement. Here, we generalize the dynamic programming approach to enhance its feasibility in three dimensions: first, the user may trade space against time; second, the proposed algorithms easily and efficiently parallelize onto thousands of processors; third, the algorithms can exploit any prior knowledge about the precedence relation on the nodes. Underlying all these results is the key observation that, given a partial order P on the nodes, an optimal DAG compatible with P can be found in time and space roughly proportional to the number of ideals of P, which can be significantly less than 2n. Considering sufficiently many carefully chosen partial orders guarantees that a globally optimal DAG will be found. Aside from the generic scheme, we present and analyze concrete tradeoff schemes based on parallel bucket orders.

关键词： exact algorithm parallelization partial order space-time tradeoff structure learning

Improving CUR matrix decomposition and the Nyström approximation via adaptive sampling

学校读者我要写书评

暂无评论

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Shusen Wang Zhihua Zhang Google MPI for Intelligent Systems College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

The CUR matrix decomposition and the Nyström approximation are two important low-rank matrix approximation techniques. The Nyström method approximates a symmetric positive semidefinite matrix in terms of a small number of its columns, while CUR approximates an arbitrary data matrix by a small number of its columns and rows. Thus, CUR decomposition can be regarded as an extension of the Nyström *** this paper we establish a more general error bound for the adaptive column/row sampling algorithm, based on which we propose more accurate CUR and Nyström algorithms with expected relative-error bounds. The proposed CUR and Nyström algorithms also have low time complexity and can avoid maintaining the whole data matrix in RAM. In addition, we give theoretical analysis for the lower error bounds of the standard Nyström method and the ensemble Nyström method. The main theoretical results established in this paper are novel, and our analysis makes no special assumption on the data matrices.

关键词： CUR matrix decomposition adaptive sampling large-scale matrix computation randomized algorithms the Nyström method

Super-polynomial quantum speed-ups for boolean evaluation trees with hidden structure 12

学校读者我要写书评

暂无评论

Super-polynomial quantum speed-ups for boolean evaluation tr...

3rd Conference on Innovations in Theoretical computer science, ITCS 2012

作者： Zhan, Bohua Kimmel, Shelby Hassidim, Avinatan Department of Mathematics Princeton University United States Center for Theoretical Physics Massachusetts Institute of Technology United States Department of Computer Science Bar Ilan University Google Israel

ISBN: (纸本)9781450311151

We give a quantum algorithm for evaluating a class of boolean formulas (such as NAND trees and 3-majority trees) on a restricted set of inputs. Due to the structure of the allowed inputs, our algorithm can evaluate a depth n tree using O(n2+logω) queries, where ω is independent of n and depends only on the type of subformulas within the tree. We also prove a classical lower bound of nΩ(log log n) queries, thus showing a (small) super-polynomial speed-up. © 2012 ACM.

关键词： Trees (mathematics)

Sublinear algorithms for penalized logistic regression in massive datasets

学校读者我要写书评

暂无评论

Sublinear algorithms for penalized logistic regression in ma...

2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2012

作者： Peng, Haoruo Wang, Zhengyu Chang, Edward Y. Zhou, Shuchang Zhang, Zhihua Google Research Beijing Beijing 100084 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Institute for Interdisciplinary Information Sciences Tsinghua University Beijing 100084 China College of Computer Science and Technology Zhejiang University Zhejiang 310027 China

ISBN: (纸本)9783642334597

Penalized logistic regression (PLR) is a widely used supervised learning model. In this paper, we consider its applications in large-scale data problems and resort to a stochastic primal-dual approach for solving PLR. In particular, we employ a random sampling technique in the primal step and a multiplicative weights method in the dual step. This technique leads to an optimization method with sublinear dependency on both the volume and dimensionality of training data. We develop concrete algorithms for PLR with 2-norm and 1-norm penalties, respectively. Experimental results over several large-scale and high-dimensional datasets demonstrate both efficiency and accuracy of our algorithms. © 2012 Springer-Verlag.

关键词： Stochastic systems