Results 11  20
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161
Object recognition with informative features and linear classification
 In ICCV
, 2003
"... In this paper we show that efficient object recognition can be obtained by combining informative features with linear classification. The results demonstrate the superiority of informative classspecific features, as compared with generic type features such as wavelets, for the task of object recogn ..."
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Cited by 96 (4 self)
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In this paper we show that efficient object recognition can be obtained by combining informative features with linear classification. The results demonstrate the superiority of informative classspecific features, as compared with generic type features such as wavelets, for the task of object recognition. We show that information rich features can reach optimal performance with simple linear separation rules, while generic feature based classifiers require more complex classification schemes. This is significant because efficient and optimal methods have been developed for spaces that allow linear separation. To compare different strategies for feature extraction, we trained and compared classifiers working in feature spaces of the same low dimensionality, using two feature types (image fragments vs. wavelets) and two classification rules (linear hyperplane and a Bayesian Network). The results show that by maximizing the individual information of the features, it is possible to obtain efficient classification by a simple linear separating rule, as well as more efficient learning. 1.
Extracting all the Randomness and Reducing the Error in Trevisan's Extractors
 In Proceedings of the 31st Annual ACM Symposium on Theory of Computing
, 1999
"... We give explicit constructions of extractors which work for a source of any minentropy on strings of length n. These extractors can extract any constant fraction of the minentropy using O(log² n) additional random bits, and can extract all the minentropy using O(log³ n) additional rando ..."
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Cited by 78 (16 self)
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We give explicit constructions of extractors which work for a source of any minentropy on strings of length n. These extractors can extract any constant fraction of the minentropy using O(log² n) additional random bits, and can extract all the minentropy using O(log³ n) additional random bits. Both of these constructions use fewer truly random bits than any previous construction which works for all minentropies and extracts a constant fraction of the minentropy. We then improve our second construction and show that we can reduce the entropy loss to 2 log(1=") +O(1) bits, while still using O(log³ n) truly random bits (where entropy loss is defined as [(source minentropy) + (# truly random bits used) (# output bits)], and " is the statistical difference from uniform achieved). This entropy loss is optimal up to a constant additive term. our...
Testing that distributions are close
 In IEEE Symposium on Foundations of Computer Science
, 2000
"... Given two distributions over an n element set, we wish to check whether these distributions are statistically close by only sampling. We give a sublinear algorithm which uses O(n 2/3 ɛ −4 log n) independent samples from each distribution, runs in time linear in the sample size, makes no assumptions ..."
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Cited by 77 (16 self)
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Given two distributions over an n element set, we wish to check whether these distributions are statistically close by only sampling. We give a sublinear algorithm which uses O(n 2/3 ɛ −4 log n) independent samples from each distribution, runs in time linear in the sample size, makes no assumptions about the structure of the distributions, and distinguishes the cases ɛ when the distance between the distributions is small (less than max ( 2 32 3 √ n, ɛ 4 √)) or large (more n than ɛ) in L1distance. We also give an Ω(n 2/3 ɛ −2/3) lower bound. Our algorithm has applications to the problem of checking whether a given Markov process is rapidly mixing. We develop sublinear algorithms for this problem as well.
Information Based Adaptive Robotic Exploration
 in Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS
, 2002
"... Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the m ..."
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Cited by 57 (0 self)
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Exploration involving mapping and concurrent localization in an unknown environment is a pervasive task in mobile robotics. In general, the accuracy of the mapping process depends directly on the accuracy of the localization process. This paper address the problem of maximizing the accuracy of the map building process during exploration by adaptively selecting control actions that maximize localisation accuracy. The map building and exploration task is modeled using an Occupancy Grid (OG) with concurrent localisation performed using a featurebased Simultaneous Localisation And Mapping (SLAM) algorithm . Adaptive sensing aims at maximizing the map information by simultaneously maximizing the expected Shannon information gain (Mutual Information) on the OG map and minimizing the uncertainty of the vehicle pose and map feature uncertainty in the SLAM process. The resulting map building system is demonstrated in an indoor environment using data from a laser scanner mounted on a mobile platform.
An Experiment in Integrated Exploration
 In Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS
, 2002
"... Integrated exploration strategy advocated in this paper refers to a tight coupling between the tasks of localization, mapping, and motion control and the effect of this coupling on the overall effectiveness of an exploration strategy. Our approach to exploration calls for a balanced evaluation of al ..."
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Cited by 41 (0 self)
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Integrated exploration strategy advocated in this paper refers to a tight coupling between the tasks of localization, mapping, and motion control and the effect of this coupling on the overall effectiveness of an exploration strategy. Our approach to exploration calls for a balanced evaluation of alternative motion actions from the point of view of information gain, localization quality, and navigation cost. To provide a uniform basis of comparison of localization quality between different locations, a "localizability" metric is introduced. It is based on the estimate of the lowest vehicle pose covariance attainable from a given location.
Similaritybased approaches to natural language processing
, 1997
"... Statistical methods for automatically extracting information about associations between words or documents from large collections of text have the potential to have considerable impact in a number of areas, such as information retrieval and naturallanguagebased user interfaces. However, even huge ..."
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Cited by 40 (3 self)
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Statistical methods for automatically extracting information about associations between words or documents from large collections of text have the potential to have considerable impact in a number of areas, such as information retrieval and naturallanguagebased user interfaces. However, even huge bodies of text yield highly unreliable estimates of the probability of relatively common events, and, in fact, perfectly reasonable events may not occur in the training data at all. This is known as the sparse data problem. Traditional approaches to the sparse data problem use crude approximations. We propose a different solution: if we are able to organize the data into classes of similar events, then, if information about an event is lacking, we can estimate its behavior from information about similar events. This thesis presents two such similaritybased approaches, where, in general, we measure similarity by the KullbackLeibler divergence, an informationtheoretic quantity. Our first approach is to build soft, hierarchical clusters: soft, because each event belongs to each cluster with some probability; hierarchical, because cluster centroids are iteratively split to model finer distinctions. Our clustering method, which uses the technique of deterministic annealing,
Realtime particle filters
 Proceedings of the IEEE
, 2004
"... ctkwok,fox£ Particle filters estimate the state of dynamical systems from sensor information. In many real time applications of particle filters, however, sensor information arrives at a significantly higher rate than the update rate of the filter. The prevalent approach to dealing with such situati ..."
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Cited by 40 (2 self)
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ctkwok,fox£ Particle filters estimate the state of dynamical systems from sensor information. In many real time applications of particle filters, however, sensor information arrives at a significantly higher rate than the update rate of the filter. The prevalent approach to dealing with such situations is to update the particle filter as often as possible and to discard sensor information that cannot be processed in time. In this paper we present realtime particle filters, which make use of all sensor information even when the filter update rate is below the update rate of the sensors. This is achieved by representing posteriors as mixtures of sample sets, where each mixture component integrates one observation arriving during a filter update. The weights of the mixture components are set so as to minimize the approximation error introduced by the mixture representation. Thereby, our approach focuses computational resources (samples) on valuable sensor information. Experiments using data collected with a mobile robot show that our approach yields strong improvements over other approaches. 1
Network Correlated Data Gathering with Explicit Communication: NPCompleteness and Algorithms
"... We consider the problem of correlated data gathering by a network with a sink node and a tree based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we ..."
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Cited by 39 (8 self)
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We consider the problem of correlated data gathering by a network with a sink node and a tree based communication structure, where the goal is to minimize the total transmission cost of transporting the information collected by the nodes, to the sink node. For source coding of correlated data, we consider a joint entropy based coding model with explicit communication where coding is simple and the transmission structure optimization is di#cult. We first formulate the optimization problem definition in the general case and then we study further a network setting where the entropy conditioning at nodes does not depend on the amount of side information, but only on its availability. We prove that even in this simple case, the optimization problem is NPhard. We propose some e#cient, scalable, and distributed heuristic approximation algorithms for solving this problem and show by numerical simulations that the total transmission cost can be significantly improved over direct transmission or the shortest path tree. We also present an approximation algorithm that provides a tree transmission structure with total cost within a constant factor from the optimal.
Distribution of mutual information
 Advances in Neural Information Processing Systems 14: Proceedings of the 2002 Conference
, 2002
"... expectation and variance of mutual information. The mutual information of two random variables ı and j with joint probabilities {πij} is commonly used in learning Bayesian nets as well as in many other fields. The chances πij are usually estimated by the empirical sampling frequency nij/n leading to ..."
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Cited by 38 (12 self)
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expectation and variance of mutual information. The mutual information of two random variables ı and j with joint probabilities {πij} is commonly used in learning Bayesian nets as well as in many other fields. The chances πij are usually estimated by the empirical sampling frequency nij/n leading to a point estimate I(nij/n) for the mutual information. To answer questions like “is I(nij/n) consistent with zero? ” or “what is the probability that the true mutual information is much larger than the point estimate? ” one has to go beyond the point estimate. In the Bayesian framework one can answer these questions by utilizing a (second order) prior distribution p(π) comprising prior information about π. From the prior p(π) one can compute the posterior p(πn), from which the distribution p(In) of the mutual information can be calculated. We derive reliable and quickly computable approximations for p(In). We concentrate on the mean, variance, skewness, and kurtosis, and noninformative priors. For the mean we also
Stochastic Inference of Regular Tree Languages
 Machine Learning
, 1999
"... this paper, we introduce a modi cation of the last algorithm that can be trained with positive samples generated according to a probabilistic production scheme. The construction follows the same guidelines as the algorithm for string languages in Carrasco and Oncina (1998). A dierent approach (Sakak ..."
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Cited by 38 (11 self)
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this paper, we introduce a modi cation of the last algorithm that can be trained with positive samples generated according to a probabilistic production scheme. The construction follows the same guidelines as the algorithm for string languages in Carrasco and Oncina (1998). A dierent approach (Sakakibara et al., 1994) generalizes the