65 research outputs found
Új módszerek az adattömörítésben = New methods in data compression
Univerzális, kis késleltetésű kódokat terveztünk individuális sorozatok veszteséges tömörítésére, melyek ugyanolyan jó teljesítményt nyújtanak, mint a sorozathoz illesztett legjobb időben változó kód egy referenciaosztályból, mely az alkalmazott kódolási eljárást időről időre változtathatja. Hatékony, kis komplexitású implementációt készítettünk arra az esetre, amikor az alap-referenciaosztály a hagyományos vagy bizonyos hálózati skalárkvantálók osztálya. Új útvonalválasztási módszereket dolgoztunk ki kommunikációs hálózatokra, melyek aszimptotikusan ugyanolyan jó QoS (csomagvesztési arány, késleltetés) eredményt adnak, mint a változó hálózati környezethez (utólag) illesztett legjobb út. Kiemelendő, hogy a módszer teljesítménye és komplexitása időben optimális konvergenciasebesség mellett a hálózat méretével (és nem az utak számával) skálázik. Kísérletek szerint az elterjedt standard bájt-alapú tömörítő algoritmusok rosszul teljesítenek, ha a forrás nem bájt-alapú, ugyanakkor a bit-alapú módszerek jól működnek bájt-alapú forrásokra is (továbbá komplexitásuk - az alkalmazott kisebb ábécé miatt - gyakran lényegesen kisebb). Ezt a megfigyelést elméletileg is igazoltuk, megvizsgálva, hogy hogyan közelíthetőek blokk-Markov-források magasabb rendű szimbólum-alapú Markov-modellek segítségével. Megoldottuk a ládapakolási probléma egy szekvenciális, on-line változatát, mely alkalmazható bizonyos, kevés erőforrással rendelkező szenzorok hatékony adásütemezésére. | We designed limited-delay data compression methods that perform asymptotically as well as the best time-varying code from a reference family (matched to the source sequence in hindsight) that can change the employed base code several times. We provided efficient, low-complexity solutions for the cases when the base reference class is the set of traditional or certain network scalar quantizers. We developed routing algorithms for communication networks that can provide asymptotically as good QoS parameters (such as packet loss ratio or delay) as the best fixed path in the network matched to the varying conditions in hindsight. The performance and complexity of the developed methods scale with the size of the network (instead of with the number of paths) even when the rate of convergence (in time) is optimal. Experiments indicate that data for which bytes are not the natural choice of symbols compress poorly using standard byte-based implementations of lossless data compression algorithms, while algorithms working on a bit level perform reasonably on byte-based data (in addition to having computational advantages resulting from operating on a small alphabet). We explained this phenomenon by analyzing how block Markov sources can be approximated with symbol-based higher order Markov sources. We provided a solution to a sequential on-line version of the bin packing problem, which can be applied to schedule transmissions for certain sensors with limited resources
Distributed Learning in Wireless Sensor Networks
The problem of distributed or decentralized detection and estimation in
applications such as wireless sensor networks has often been considered in the
framework of parametric models, in which strong assumptions are made about a
statistical description of nature. In certain applications, such assumptions
are warranted and systems designed from these models show promise. However, in
other scenarios, prior knowledge is at best vague and translating such
knowledge into a statistical model is undesirable. Applications such as these
pave the way for a nonparametric study of distributed detection and estimation.
In this paper, we review recent work of the authors in which some elementary
models for distributed learning are considered. These models are in the spirit
of classical work in nonparametric statistics and are applicable to wireless
sensor networks.Comment: Published in the Proceedings of the 42nd Annual Allerton Conference
on Communication, Control and Computing, University of Illinois, 200
Consistency in Models for Distributed Learning under Communication Constraints
Motivated by sensor networks and other distributed settings, several models
for distributed learning are presented. The models differ from classical works
in statistical pattern recognition by allocating observations of an independent
and identically distributed (i.i.d.) sampling process amongst members of a
network of simple learning agents. The agents are limited in their ability to
communicate to a central fusion center and thus, the amount of information
available for use in classification or regression is constrained. For several
basic communication models in both the binary classification and regression
frameworks, we question the existence of agent decision rules and fusion rules
that result in a universally consistent ensemble. The answers to this question
present new issues to consider with regard to universal consistency. Insofar as
these models present a useful picture of distributed scenarios, this paper
addresses the issue of whether or not the guarantees provided by Stone's
Theorem in centralized environments hold in distributed settings.Comment: To appear in the IEEE Transactions on Information Theor
The on-line shortest path problem under partial monitoring
The on-line shortest path problem is considered under various models of
partial monitoring. Given a weighted directed acyclic graph whose edge weights
can change in an arbitrary (adversarial) way, a decision maker has to choose in
each round of a game a path between two distinguished vertices such that the
loss of the chosen path (defined as the sum of the weights of its composing
edges) be as small as possible. In a setting generalizing the multi-armed
bandit problem, after choosing a path, the decision maker learns only the
weights of those edges that belong to the chosen path. For this problem, an
algorithm is given whose average cumulative loss in n rounds exceeds that of
the best path, matched off-line to the entire sequence of the edge weights, by
a quantity that is proportional to 1/\sqrt{n} and depends only polynomially on
the number of edges of the graph. The algorithm can be implemented with linear
complexity in the number of rounds n and in the number of edges. An extension
to the so-called label efficient setting is also given, in which the decision
maker is informed about the weights of the edges corresponding to the chosen
path at a total of m << n time instances. Another extension is shown where the
decision maker competes against a time-varying path, a generalization of the
problem of tracking the best expert. A version of the multi-armed bandit
setting for shortest path is also discussed where the decision maker learns
only the total weight of the chosen path but not the weights of the individual
edges on the path. Applications to routing in packet switched networks along
with simulation results are also presented.Comment: 35 page
Efficient Learning of Sparse Conditional Random Fields for Supervised Sequence Labelling
Conditional Random Fields (CRFs) constitute a popular and efficient approach
for supervised sequence labelling. CRFs can cope with large description spaces
and can integrate some form of structural dependency between labels. In this
contribution, we address the issue of efficient feature selection for CRFs
based on imposing sparsity through an L1 penalty. We first show how sparsity of
the parameter set can be exploited to significantly speed up training and
labelling. We then introduce coordinate descent parameter update schemes for
CRFs with L1 regularization. We finally provide some empirical comparisons of
the proposed approach with state-of-the-art CRF training strategies. In
particular, it is shown that the proposed approach is able to take profit of
the sparsity to speed up processing and hence potentially handle larger
dimensional models
A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework
This paper develops a new algorithm for inducing cost-sensitive decision trees that is inspired by the multi-armed bandit problem, in which a player in a casino has to decide which slot machine (bandit) from a selection of slot machines is likely to pay out the most. Game Theory proposes a solution to this multi-armed bandit problem by using a process of exploration and exploitation in which reward is maximized. This paper utilizes these concepts to develop a new algorithm by viewing the rewards as a reduction in costs, and utilizing the exploration and exploitation techniques so that a compromise between decisions based on accuracy and decisions based on costs can be found. The algorithm employs the notion of lever pulls in the multi-armed bandit game to select the attributes during decision tree induction, using a look-ahead methodology to explore potential attributes and exploit the attributes which maximizes the reward. The new algorithm is evaluated on fifteen datasets and compared to six well-known algorithms J48, EG2, MetaCost, AdaCostM1, ICET and ACT. The results obtained show that the new multi-armed based algorithm can produce more cost-effective trees without compromising accuracy. The paper also includes a critical appraisal of the limitations of the new algorithm and proposes avenues for further research
Pure exploration in multi-armed bandits with low rank structure using oblivious sampler
In this paper, we consider the low rank structure of the reward sequence of
the pure exploration problems. Firstly, we propose the separated setting in
pure exploration problem, where the exploration strategy cannot receive the
feedback of its explorations. Due to this separation, it requires that the
exploration strategy to sample the arms obliviously. By involving the kernel
information of the reward vectors, we provide efficient algorithms for both
time-varying and fixed cases with regret bound . Then, we
show the lower bound to the pure exploration in multi-armed bandits with low
rank sequence. There is an gap between our upper bound and
the lower bound.Comment: 15 page
Modeling Driver Behavior From Demonstrations in Dynamic Environments Using Spatiotemporal Lattices
International audienceOne of the most challenging tasks in the development of path planners for intelligent vehicles is the design of the cost function that models the desired behavior of the vehicle. While this task has been traditionally accomplished by hand-tuning the model parameters, recent approaches propose to learn the model automatically from demonstrated driving data using Inverse Reinforcement Learning (IRL). To determine if the model has correctly captured the demonstrated behavior, most IRL methods require obtaining a policy by solving the forward control problem repetitively. Calculating the full policy is a costly task in continuous or large domains and thus often approximated by finding a single trajectory using traditional path-planning techniques. In this work, we propose to find such a trajectory using a conformal spatiotemporal state lattice, which offers two main advantages. First, by conforming the lattice to the environment, the search is focused only on feasible motions for the robot, saving computational power. And second, by considering time as part of the state, the trajectory is optimized with respect to the motion of the dynamic obstacles in the scene. As a consequence, the resulting trajectory can be used for the model assessment. We show how the proposed IRL framework can successfully handle highly dynamic environments by modeling the highway tactical driving task from demonstrated driving data gathered with an instrumented vehicle
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