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Online Markov decision processes under bandit feedback 10

Online Markov decision processes under bandit feedback

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Proceedings of the 24th International Conference on Neural Information Processing Systems - Volume 2

作者： Gergely Neu András György Csaba Szepesvári András Antos Department of Computer Science and Information Theory Budapest University of Technology and Economics Hungary Machine Learning Research Group MTA SZTAKI Institute for Computer Science and Control Hungary Department of Computing Science University of Alberta Canada Machine Learning Research Group MTA SZTAKI Institute for Computer Science and Control Hungary

We consider online learning in finite stochastic Markovian environments where in each time step a new reward function is chosen by an oblivious adversary. The goal of the learning agent is to compete with the best stationary policy in terms of the total reward received. In each time step the agent observes the current state and the reward associated with the last transition, however, the agent does not observe the rewards associated with other state-action pairs. The agent is assumed to know the transition probabilities. The state of the art result for this setting is a no-regret algorithm. In this paper we propose a new learning algorithm and, assuming that stationary policies mix uniformly fast, we show that after T time steps, the expected regret of the new algorithm is O (T2/3 (ln T)1/3), giving the first rigorously proved regret bound for the problem.

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A new instance selection algorithm based on contribution for nearest neighbour classification

A new instance selection algorithm based on contribution for...

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International Conference on machine learning and Cybernetics

作者： Cai, Yong-Hua Wu, Bo He, Yu-Lin Zhang, Ye Department of Mathematics and Computer Science Hebei Normal University for Nationalities Chengde067000 Hebei China Automation Department Chengde Iron and Steel Branch Hebei Iron and Steel Group Co. Ltd. Chengde 067000 Hebei China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071002 Hebei China

ISBN: (纸本)9781424465262

Nearest Neighbor Classifier is one of the most classical lazy learning schemes. The basic nearest neighbor classifiers suffer from the common problem that the instances used to train the classifier are all stored indiscriminately, and as a result, the required memory storage is huge and response time becomes slow with a large database. In this paper, a new Instances Selection algorithm based on Classification Contribution Function shortly named ISCC is presented. In this algorithm, a function is introduced to evaluate the classification ability of the instances. For each instance, the function considers its contribution to the neighbor instances not only with same class but also with different class. Then an instance with the highest value of Classification Contribution Function is added to the condensed subset and the instances which can be classified correctly are deleted in each iteration. This process is repeated until the subset is no longer getting larger. The time complexity of ISCC is Ο (in2). The experimental results on two artificial databases and some real databases demonstrate the effectiveness and the feasibility of the proposed algorithm. Compared to traditional methods, such as MCS, ICF and ENN, the condensed sets obtained by ISCC is superior in storage and classification accuracy. © 2010 IEEE.

关键词： Classification (of information)

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Efficient protein-ligand docking using sustainable evolutionary algorithms

Efficient protein-ligand docking using sustainable evolution...

引用

International Conference on Hybrid Intelligent Systems (HIS)

作者： Emrah Atilgan Jianjun Hu Machine Learning and Evolution Laboratory Department of Computer Science and Engineering University of South Carolina Columbia SC USA

AutoDock is a widely used automated protein docking program in structure-based drug-design. Different search algorithms such as simulated annealing, traditional genetic algorithm (GA) and Lamarckian genetic algorithm (LGA) are implemented in AutoDock. However, the docking performance of these algorithms is still limited by the local optima issue of simulated annealing or the premature convergence issue typical in traditional evolutionary algorithms (EA). Due to the stochastic nature of these search algorithms, users usually need to run multiple times to get reasonable docking results, which is time-consuming. We have developed a new docking program AutoDockX by applying a sustainable GA, Age-Layered Population Structure (ALPS) to the protein docking problem. We tested the docking performance over three different proteins (pr, cox and hsp90) with more than 20 candidate ligands for each protein. Our experiments showed that the sustainable GA based AutodockX achieved significantly better docking performance in terms of running time and robustness than all the existing search algorithms implemented in the latest version of AutoDock. AutodockX thus has unique advantages in large-scale virtual screening.

关键词： Proteins Gallium Evolutionary computation Simulated annealing Genetic algorithms Algorithm design and analysis Convergence

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2-Stage instance selection algorithm for KNN based on Nearest Unlike Neighbors

2-Stage instance selection algorithm for KNN based on Neares...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Chun-Ru Dong Patrick P. K. Chan Wing W. Y. Ng Daniel S. Yeung Department of Mathematics and Computer Science Hebei University Baoding China Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology Guangzhou China

For the virtues such as simplicity, high generalization capability, and few training cost, the K-Nearest-Neighbor (KNN) classifier is widely used in pattern recognition and machine learning. However, the computation complexity of KNN classifier will become higher when dealing with large data sets classification problem. In consequence, its efficiency will be decreased greatly. This paper proposes a general two-stage training set condensing algorithm for general KNN classifier. First, we identify the noise data points and remove them from the original training set. Second, a general condensed nearest neighbor rule based on the so-called Nearest Unlike Neighbor (NUN) is presented to further eliminate the redundant samples in training set. In order to verify the performance of the proposed method, some numerical experiments are conducted on several UCI benchmark databases.

关键词： Training Classification algorithms Nearest neighbor searches Artificial neural networks Testing Noise Noise measurement

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Pool-based active learning based on incremental decision tree

Pool-based active learning based on incremental decision tre...

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International Conference on machine learning and Cybernetics

作者： Wang, Shuo Wang, Jian-Jian Gao, Xiang-Hui Wang, Xue-Zheng Machine Learning Center Faculty of Mathematics and Computer Science Hebei University Baoding 071002 China Department of Electronics and Communication Engineering North China Electric Power University Baoding 071003 China Defending Faculty Political Ministry of the 68307 Army Zhangye in Gansu 734000 China

ISBN: (纸本)9781424465262

The pool-based active learning intends to collect the samples into the pool firstly, and selects the best informative sample from it which has no label to add into the training sets for updating the classifier secondly. This paper proposed a new method based on the incremental decision tree algorithm to measure the ambiguity of the unlabeled samples for the sample selection in the active learning. © 2010 IEEE.

关键词： Decision trees

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Construction of radial basis function neural networks via a minimization of its localized generalization error

Construction of radial basis function neural networks via a ...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Daniel S. Yeung Bin-Bin Sun Wing W. Y. Ng Patrick P. K. Chan Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology China Department of Computer Science Harbin Institute of Technology Harbin Institute of Technology China

Lots of researchers have been studying on how to construct radial basis function neural networks. To determine the number and location of hidden neurons, a recursive procedure is adopted with a new evaluation criterion based on localized generalization error model (L-GEM). We derive a new sensitivity expression for Gaussian radial basis function neural network based on L-GEM, and get a new localized generalization error bound. The RBF that yields the minimal localized generalization error bound is selected. We compare our approach with minimization of cross validation, and minimization of training mean square error (MSE) methods. The experimental results show that our approach performs much better than the other two methods with reasonable number of centers.

关键词： Training Sensitivity Artificial neural networks Neurons Testing Accuracy machine learning

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Sensitivity analysis of multilayer percetron based on elastic function

Sensitivity analysis of multilayer percetron based on elasti...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Chun-Guo Li Hai-Feng Li Yu-Fen Zhang Qun-Feng Zhang Machine Learning Center Faculty of Mathematics and Computer Science Hebei University Baoding China Department of Educational Administration Hebei University Baoding China

The sensitivity analysis can help to construct a tightly neural network. There are several methods to define the sensitivity of input and weight for perturbations to the trained neural network. This paper proposed a sensitivity definition based on elastic function. This definition considers the measure of the variable of the reference network. The sensitivity calculating formulae are deduced for perceptron and MLP.

关键词： Artificial neural networks Neurons Sensitivity analysis machine learning Cybernetics Multilayer perceptrons

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Expectation propagation for bayesian multi-task feature selection

Expectation propagation for bayesian multi-task feature sele...

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European Conference on machine learning and Principles and Practice of Knowledge Discovery in databases, ECML PKDD 2010

作者： Hernández-Lobato, Daniel Hernández-Lobato, José Miguel Helleputte, Thibault Dupont, Pierre Machine Learning Group ICTEAM Institute Université Catholique de Louvain Place Sainte Barbe 2 B-1348 Louvain-la-Neuve Belgium Computer Science Department Universidad Autónoma de Madrid C/ Francisco Tomás y Valiente 11 28049 Madrid Spain

ISBN: (纸本)364215879X

In this paper we propose a Bayesian model for multi-task feature selection. This model is based on a generalized spike and slab sparse prior distribution that enforces the selection of a common subset of features across several tasks. Since exact Bayesian inference in this model is intractable, approximate inference is performed through expectation propagation (EP). EP approximates the posterior distribution of the model using a parametric probability distribution. This posterior approximation is particularly useful to identify relevant features for prediction. We focus on problems for which the number of features d is significantly larger than the number of instances for each task. We propose an efficient parametrization of the EP algorithm that offers a computational complexity linear in d. Experiments on several multi-task datasets show that the proposed model outperforms baseline approaches for single-task learning or data pooling across all tasks, as well as two state-of-the-art multi-task learning approaches. Additional experiments confirm the stability of the proposed feature selection with respect to various sub-samplings of the training data. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Probability distributions

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Semantic clustering using multiple ontologies

Semantic clustering using multiple ontologies

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作者： Batet, Montserrat Valls, Aïda Gibert, Karina Sánchez, David Research Group Department of Computer Science and Mathematics Universitat Rovira i Virgili Avda. Països Catalans 26 43007 Tarragona Catalunya Spain Knowledge Engineering and Machine Learning Group Department of Statistics and Operations Research Universitat Politècnica de Catalunya Spain

ISBN: (纸本)9781607506423

data mining tools able to semantically interpret textual or linguistic data are acquiring a growing importance. Moreover, the development of large ontologies for general and specific domains provides new tools to include background knowledge into data mining techniques such as clustering. In this paper we present an unsupervised clustering method that exploits the semantics of categorical data by means of ontologies, and it is also able to manage numerical data. Our method is able to use different ontologies in order to assess the meaning of the values during the clustering process, leading to a set of clusters with a clearer semantic interpretation in a particular domain. The influence of using one or several ontologies is analyzed by using real data collected from visitors to the Ebre Delta Natural Park, which is a protected natural reserve in Catalonia (Spain). © 2010 The authors and IOS Press. All rights reserved.

关键词： Ontology

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I want to go home: Empowering the lost mobile device

I want to go home: Empowering the lost mobile device

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Chi Zhang Robert W.H. Fisher Joel Wein Department of Computer Science and Engineering Polytechnic Institute of New York University Brooklyn NY USA Department of Machine Learning Carnegie Mellon University Pittsburgh PA USA

It is estimated that over 8 million cell phones are lost or stolen each year [7]; often the loss of a cell phone means the loss of personal data, time and enormous aggravation. In this paper we present machine-learning based algorithms by which a cell phone can discern that it may be lost, and take steps to enhance its chances of being successfully recovered. We use data collected from the Reality Mining project [10] to create a suite of realistic test cases that model lost cell phone behavior. On these data sets our best algorithms can identify cases of a lost mobile device, based on its behavior over the previous 3 hours, with close to 100% accuracy. In addition, the algorithm generates false positive identifications with probability less than 3%; for individuals with relatively predictable lifestyles the False Positive Rate is substantially less. We also use the Reality Mining data to construct a set of test cases that model the behavior of a stolen phone, and show that similar algorithmic techniques give reasonable results in this setting as well.

关键词： Entropy Poles and towers Heuristic algorithms Mobile handsets Cellular phones Training Artificial neural networks

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