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LIFT: Multi-label learning with label-specific features

LIFT: Multi-label learning with label-specific features

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22nd international Joint conference on Artificial Intelligence, IJCAI 2011

作者： Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing 210096 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing 210093 China

ISBN: (纸本)9781577355120

Multi-label learning deals with the problem where each training example is represented by a single instance while associated with a set of class labels. For an unseen example, existing approaches choose to determine the membership of each possible class label to it based on identical feature set, i.e. the very instance representation of the unseen example is employed in the discrimination processes of all labels. However, this commonly-used strategy might be suboptimal as different class labels usually carry specific characteristics of their own, and it could be beneficial to exploit different feature sets for the discrimination of different labels. Based on the above reflection, we propose a new strategy to multi-label learning by leveraging label-specific features, where a simple yet effective algorithm named LIFT is presented. Briefly, LIFT constructs features specific to each label by conducting clustering analysis on its positive and negative instances, and then performs training and testing by querying the clustering results. Extensive experiments across sixteen diversified data sets clearly validate the superiority of LIFT against other well-established multi-label learning algorithms.

关键词： Clustering algorithms

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Robotic object detection: learning to improve the classifiers using sparse graphs for path planning

Robotic object detection: Learning to improve the classifier...

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22nd international Joint conference on Artificial Intelligence, IJCAI 2011

作者： Jia, Zhaoyin Saxena, Ashutosh Chen, Tsuhan School of Electrical and Computer Engineering Cornell University United States Department of Computer Science Cornell University United States

ISBN: (纸本)9781577355120

Object detection is a basic skill for a robot to perform tasks in human environments. In order to build a good object classifier, a large training set of labeled images is required;this is typically collected and labeled (often painstakingly) by a human. This method is not scalable and therefore limits the robot's detection performance. We propose an algorithm for a robot to collect more data in the environment during its training phase so that in the future it could detect objects more reliably. The first step is to plan a path for collecting additional training images, which is hard because a previously visited location affects the decision for the future locations. One key component of our work is path planning by building a sparse graph that captures these dependencies. The other key component is our learning algorithm that weighs the errors made in robot's data collection process while updating the classifier. In our experiments, we show that our algorithms enable the robot to improve its object classifiers significantly.

关键词： Graph theory

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Simulating visitors recreational activities in a virtual environment

Simulating visitors recreational activities in a virtual env...

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22nd IASTED international conference on Modelling and Simulation, MS 2011

作者： Chen, Yiqun Bishop, Ian D. Department of Infrastructure Engineering University of Melbourne VIC 3010 Australia

ISBN: (纸本)9780889868878

In the context of urban park management, knowledge of visitor recreational activities is imperative to planners to design and manage their lands and facilities effectively. A well calibrated Multi-Agent System (MAS) for visitor simulation can offer planners a better understanding either in the current situation or in any proposed scenarios. In addition, by reconstructing scenes based on Geographic Information System (GIS) data and detailed modeling, 3D Virtual Environments (VE) provide an intuitive and vivid way to mimic the real world and fundamentally extend agent perception and exploring abilities. This paper reports the approaches of using a multi-agent model in a virtual environment to simulate visitor activities in Australian Garden (AG), Royal Botanic Gardens Cranbourne (RBGC). To understand how visitors interact with landscapes, an iPhone-based visitor survey is conducted to collect on-site data;then a 3D virtual environment is constructed using GIS data based on Torque Game Engine Advanced (TGEA);a navigation mesh and attraction point structure are introduced to broaden agent movements;finally, equipped with vision depth detection, attraction point reasoning and landscape aesthetic ability, an intelligent agent model is proposed.

关键词： Multi agent systems

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Source-selection-free transfer learning

Source-selection-free transfer learning

引用

22nd international Joint conference on Artificial Intelligence, IJCAI 2011

作者： Xiang, Evan Wei Pan, Sinno Jialin Pan, Weike Su, Jian Yang, Qiang Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong Institute for Infocomm Research 1 Fusionopolis Way #21-01 Connexis Singapore 138632 Singapore

ISBN: (纸本)9781577355120

Transfer learning addresses the problems that labeled training data are insufficient to produce a high-performance model. Typically, given a target learning task, most transfer learning approaches require to select one or more auxiliary tasks as sources by the designers. However, how to select the right source data to enable effective knowledge transfer automatically is still an unsolved problem, which limits the applicability of transfer learning. In this paper, we take one step ahead and propose a novel transfer learning framework, known as source-selection-free transfer learning (SSFTL), to free users from the need to select source domains. Instead of asking the users for source and target data pairs, as traditional transfer learning does, SSFTL turns to some online information sources such as World Wide Web or the Wikipedia for help. The source data for transfer learning can be hidden somewhere within this large online information source, but the users do not know where they are. Based on the online information sources, we train a large number of classifiers. Then, given a target task, a bridge is built for labels of the potential source candidates and the target domain data in SSFTL via some large online social media with tag cloud as a label translator. An added advantage of SSFTL is that, unlike many previous transfer learning approaches, which are difficult to scale up to the Web scale, SSFTL is highly scalable and can offset much of the training work to offline stage. We demonstrate the effectiveness and efficiency of SSFTL through extensive experiments on several real-world datasets in text classification.

关键词： Social networking (online)

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Plan recognition in virtual laboratories

Plan recognition in virtual laboratories

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22nd international Joint conference on Artificial Intelligence, IJCAI 2011

作者： Amir, Ofra Gal, YA'Akov Department of Information Systems Engineering Faculty of Engineering Sciences Ben-Gurion University of the Negev Israel

ISBN: (纸本)9781577355120

This paper presents a plan recognition algorithm for inferring student behavior using virtual science laboratories. The algorithm extends existing plan recognition technology and was integrated with an existing educational application for chemistry. Automatic recognition of students' activities in virtual laboratories can provide important information to teachers as well as serve as the basis for intelligent tutoring. Student use of virtual laboratories presents several challenges: Students may repeat activities indefinitely, interleave between activities, and engage in exploratory behavior using trial-and-error. The plan recognition algorithm uses a recursive grammar that heuristically generates plans on the fly, taking into account chemical reactions and effects to determine students' intended high-level actions. The algorithm was evaluated empirically on data obtained from college students using virtual laboratory software for teaching chemistry. Results show that the algorithm was able to (1) infer the plans used by students to construct their models;(2) recognize such key processes as titration and dilution when they occurred in students' work;(3) identify partial solutions;(4) isolate sequences of actions that were part of a single error.

关键词： Students

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Distribution-aware online classifiers

Distribution-aware online classifiers

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22nd international Joint conference on Artificial Intelligence, IJCAI 2011

作者： Nguyen, Tam T. Chang, Kuiyu Hui, Siu Cheung School of Computer Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Singapore

ISBN: (纸本)9781577355120

We propose a family of Passive-Aggressive Mahalanobis (PAM) algorithms, which are incremental (online) binary classifiers that consider the distribution of data. PAM is in fact a generalization of the Passive-Aggressive (PA) algorithms to handle data distributions that can be represented by a co-variance matrix. The update equations for PAM are derived and theoretical error loss bounds computed. We benchmarked PAM against the original PA-I, PA-II, and Confidence Weighted (CW) learning. Although PAM somewhat resembles CWin its update equations, PA minimizes differences in the weights while CW minimizes differences in weight distributions. Results on 8 classification datasets, which include a real-life micro-blog sentiment classification task, show that PAM consistently out performed its competitors, most notably CW. This shows that a simple approach like PAM is more practical in real-life classification tasks, compared to more sophisticated approaches like CW.

关键词： Classification (of information)

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Competing against the Best Nearest Neighbor Filter in Regression

Competing against the Best Nearest Neighbor Filter in Regres...

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22nd international conference on Algorithmic learning Theory (ALT 2011)

作者： Dalalyan, Arnak S. Salmon, Joseph Université Paris Est Ecole des Ponts ParisTech 77455 Marne-la-Vallée Cedex 2 France Electrical and Computer Engineering Duke University Durham NC 27708 7P.O. Box 90291 United States

ISBN: (纸本)9783642244117;9783642244124

Designing statistical procedures that are provably almost as accurate as the best one in a given family is one of central topics in statistics and learning theory. Oracle inequalities offer then a convenient theoretical framework for evaluating different strategies, which can be roughly classified into two classes: selection and aggregation strategies. The ultimate goal is to design strategies satisfying oracle inequalities with leading constant one and rate-optimal residual term. In many recent papers, this problem is addressed in the case where the aim is to beat the best procedure from a given family of linear smoothers. However, the theory developed so far either does not cover the important case of nearest-neighbor smoothers or provides a suboptimal oracle inequality with a leading constant considerably larger than one. In this paper, we prove a new oracle inequality with leading constant one that is valid under a general assumption on linear smoothers allowing, for instance, to compete against the best nearest-neighbor filters.

关键词： adaptive smoothing nonparametric regression supervised learning

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Adaptation of a mixture of multivariate Bernoulli distributions

Adaptation of a mixture of multivariate Bernoulli distributi...

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22nd international Joint conference on Artificial Intelligence, IJCAI 2011

作者： Kamthe, Ankur Carreira-Perpĩ´nan, Miguel Á. Cerpa, Alberto E. Electrical Engineering and Computer Science University of California Merced CA United States

ISBN: (纸本)9781577355120

The mixture of multivariate Bernoulli distributions (MMB) is a statistical model for high-dimensional binary data in widespread use. Recently, the MMB has been used to model the sequence of packet receptions and losses of wireless links in sensor networks. Given an MMB trained on long data traces recorded from links of a deployed network, one can then use samples from the MMB to test different routing algorithms for as long as desired. However, learning an accurate model for a new link requires collecting from it long traces over periods of hours, a costly process in practice (e.g. limited battery life). We propose an algorithm that can adapt a preexisting MMB trained with extensive data to a new link from which very limited data is available. Our approach constrains the new MMB's parameters through a nonlinear transformation of the existing MMB's parameters. The transformation has a small number of parameters that are estimated using a generalized EM algorithm with an inner loop of BFGS iterations. We demonstrate the efficacy of the approach using the MNIST dataset of handwritten digits, and wireless link data from a sensor network. We show we can learn accurate models from data traces of about 1 minute, about 10 times shorter than needed if training an MMB from scratch.

关键词： Mixtures

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Improving stock market prediction by integrating both market news and stock prices

Improving stock market prediction by integrating both market...

引用

22nd international conference on database and Expert Systems Applications, DEXA 2011

作者： Li, Xiaodong Wang, Chao Dong, Jiawei Wang, Feng Deng, Xiaotie Zhu, Shanfeng Department of Computer Science City University Hong Kong Hong Kong Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai 200433 China School of Computer State Key Lab of Software Engineering Wuhan University Wuhan 430072 China Department of Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9783642230905

Stock market is an important and active part of nowadays financial markets. Addressing the question as to how to model financial information from two sources, we focus on improving the accuracy of a computer aided prediction by combining information hidden in market news and stock prices in this study. Using the multi-kernel learning technique, a system is presented that makes predictions for the Hong Kong stock market by incorporating those two information sources. Experiments were conducted and the results have shown that in both cross validation and independent testing, our system has achieved better directional accuracy than those by the baseline system that is based on single one information source, as well as by the system that integrates information sources in a simple way. © 2011 Springer-Verlag Berlin Heidelberg.

关键词： Forecasting

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Transfer learning to predict missing ratings via heterogeneous user feedbacks

Transfer learning to predict missing ratings via heterogeneo...

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22nd international Joint conference on Artificial Intelligence, IJCAI 2011

作者： Pan, Weike Liu, Nathan N. Xiang, Evan W. Yang, Qiang Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong

ISBN: (纸本)9781577355120

data sparsity due to missing ratings is a major challenge for collaborative filtering (CF) techniques in recommender systems. This is especially true for CF domains where the ratings are expressed numerically. We observe that, while we may lack the information in numerical ratings, we may have more data in the form of binary ratings. This is especially true when users can easily express themselves with their likes and dislikes for certain items. In this paper, we explore how to use the binary preference data expressed in the form of like/dislike to help reduce the impact of data sparsity of more expressive numerical ratings. We do this by transferring the rating knowledge from some auxiliary data source in binary form (that is, likes or dislikes), to a target numerical rating matrix. Our solution is to model both numerical ratings and like/dislike in a principled way, using a novel framework of Transfer by Collective Factorization (TCF). In particular, we construct the shared latent space collectively and learn the data-dependent effect separately. A major advantage of the TCF approach over previous collective matrix factorization (or bifactorization) methods is that we are able to capture the data-dependent effect when sharing the data-independent knowledge, so as to increase the overall quality of knowledge transfer. Experimental results demonstrate the effectiveness of TCF at various sparsity levels as compared to several state-of-the-art methods.

关键词： Collaborative filtering

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