Results 1  10
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23
MDLbased Decision Tree Pruning
, 1995
"... This paper explores the application of the Minimum Description Length principle for pruning decision trees. We present a new algorithm that intuitively captures the primary goal of reducing the misclassification error. An experimental comparison is presented with three other pruning algorithms. The ..."
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Cited by 63 (1 self)
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This paper explores the application of the Minimum Description Length principle for pruning decision trees. We present a new algorithm that intuitively captures the primary goal of reducing the misclassification error. An experimental comparison is presented with three other pruning algorithms. The results show that the MDL pruning algorithm achieves good accuracy, small trees, and fast execution times. Introduction Construction or "induction" of decision trees from examples has been the subject of extensive research in the past [Breiman et. al. 84, Quinlan 86]. It is typically performed in two steps. First, training data is used to grow a decision tree. Then in the second step, called pruning, the tree is reduced to prevent "overfitting". There are two broad classes of pruning algorithms. The first class includes algorithms like costcomplexity pruning [Breiman et. al., 84], that use a separate set of samples for pruning, distinct from the set used to grow the tree. In many cases, ...
Precise Minimax Redundancy and Regret
 IEEE TRANS. INFORMATION THEORY
, 2004
"... Recent years have seen a resurgence of interest in redundancy of lossless coding. The redundancy (regret) of universal xed{to{variable length coding for a class of sources determines by how much the actual code length exceeds the optimal (ideal over the class) code length. In a minimax scenario ..."
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Cited by 33 (13 self)
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Recent years have seen a resurgence of interest in redundancy of lossless coding. The redundancy (regret) of universal xed{to{variable length coding for a class of sources determines by how much the actual code length exceeds the optimal (ideal over the class) code length. In a minimax scenario one nds the best code for the worst source either in the worst case (called also maximal minimax) or on average. We rst study the worst case minimax redundancy over a class of stationary ergodic sources and replace Shtarkov's bound by an exact formula. Among others, we prove that a generalized Shannon code minimizes the worst case redundancy, derive asymptotically its redundancy, and establish some general properties. This allows us to obtain precise redundancy rates for memoryless, Markov and renewal sources. For example, we derive the exact constant of the redundancy rate for memoryless and Markov sources by showing that an integer nature of coding contributes log(log m=(m 1))= log m+ o(1) where m is the size of the alphabet. Then we deal with the average minimax redundancy and regret. Our approach
Universal compression of memoryless sources over unknown alphabets
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 2004
"... It has long been known that the compression redundancy of independent and identically distributed (i.i.d.) strings increases to infinity as the alphabet size grows. It is also apparent that any string can be described by separately conveying its symbols, and its pattern—the order in which the symbol ..."
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Cited by 32 (10 self)
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It has long been known that the compression redundancy of independent and identically distributed (i.i.d.) strings increases to infinity as the alphabet size grows. It is also apparent that any string can be described by separately conveying its symbols, and its pattern—the order in which the symbols appear. Concentrating on the latter, we show that the patterns of i.i.d. strings over all, including infinite and even unknown, alphabets, can be compressed with diminishing redundancy, both in block and sequentially, and that the compression can be performed in linear time. To establish these results, we show that the number of patterns is the Bell number, that the number of patterns with a given number of symbols is the Stirling number of the second kind, and that the redundancy of patterns can be bounded using results of Hardy and Ramanujan on the number of integer partitions. The results also imply an asymptotically optimal solution for the GoodTuring probabilityestimation problem.
Efficient Bayesian Parameter Estimation in Large Discrete Domains
 Advances in Neural Information Processing Systems
, 1999
"... In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes, most of which do not appear in the training data. We analyze this problem from a Bayesian perspective and develop a hierarchical prior that incorporates the assum ..."
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Cited by 29 (1 self)
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In this paper we examine the problem of estimating the parameters of a multinomial distribution over a large number of discrete outcomes, most of which do not appear in the training data. We analyze this problem from a Bayesian perspective and develop a hierarchical prior that incorporates the assumption that the observed outcomes constitute only a small subset of the possible outcomes. We show how to efficiently perform exact inference with this form of hierarchical prior and compare our method to standard approaches and demonstrate its merits. Category: Algorithms and Architectures Presentation preference: none This paper was not submitted elsewhere nor will be submitted during NIPS review period. 1 Introduction One of the most important problems in statistical inference is multinomialestimation: Given a past history of observations independent trials with a discrete set of outcomes, predict the probability of the next trial. Such estimators are the basic building blocks in mor...
Switching Portfolios
 International Journal of Neural Systems
, 1998
"... Recently, there has been work on online portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight [2, 6, 3]. By their nature, these algorithms employ the assumption that high yield returns can be achieved using a fixed asset allocation ..."
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Cited by 28 (1 self)
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Recently, there has been work on online portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight [2, 6, 3]. By their nature, these algorithms employ the assumption that high yield returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the return of a constant rebalanced portfolio is much smaller than the return of an adhoc investment strategy that adapts to changes in the market. In this paper we present an efficient portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including a fixed percentage transaction cost which was recently investigated [1]. We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22year perio...
An Efficient Extension to Mixture Techniques for Prediction and Decision Trees
 Machine Learning
, 1999
"... We present an e#cient method for maintaining mixtures of prunings of a prediction or decision tree that extends the previous methods for "nodebased" prunings (Buntine, 1990; Willems, Shtarkov, & Tjalkens, 1995; Helmbold & Schapire, 1997; Singer, 1997) to the larger class of edgebased prunings. The ..."
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Cited by 27 (4 self)
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We present an e#cient method for maintaining mixtures of prunings of a prediction or decision tree that extends the previous methods for "nodebased" prunings (Buntine, 1990; Willems, Shtarkov, & Tjalkens, 1995; Helmbold & Schapire, 1997; Singer, 1997) to the larger class of edgebased prunings. The method includes an online weightallocation algorithm that can be used for prediction, compression and classification. Although the set of edgebased prunings of a given tree is much larger than that of nodebased prunings, our algorithm has similar space and time complexity to that of previous mixture algorithms for trees. Using the general online framework of Freund & Schapire (1997), we prove that our algorithm maintains correctly the mixture weights for edgebased prunings with any bounded loss function. We also give a similar algorithm for the logarithmic loss function with a corresponding weightallocation algorithm. Finally, we describe experiments comparing nodebased and edgebased mixture models for estimating the probability of the next word in English text, which show the advantages of edgebased models. Keywords: mixture models, decision and prediction trees, online learning, statistical language modeling 1.
Pointwise Redundancy in Lossy Data Compression and Universal Lossy Data Compression
 IEEE Trans. Inform. Theory
, 1999
"... We characterize the achievable pointwise redundancy rates for lossy data compression at a fixed distortion level. "Pointwise redundancy" refers to the difference between the description length achieved by an nthorder block code and the optimal nR(D) bits. For memoryless sources, we show that the be ..."
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Cited by 21 (13 self)
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We characterize the achievable pointwise redundancy rates for lossy data compression at a fixed distortion level. "Pointwise redundancy" refers to the difference between the description length achieved by an nthorder block code and the optimal nR(D) bits. For memoryless sources, we show that the best achievable redundancy rate is of order O( p n) in probability. This follows from a secondorder refinement to the classical source coding theorem, in the form of a "onesided central limit theorem." Moreover, we show that, along (almost) any source realization, the description lengths of any sequence of block codes operating at distortion level D exceed nR(D) by at least as much as C p n log log n, infinitely often. Corresponding direct coding theorems are also given, showing that these rates are essentially achievable. The above rates are in sharp contrast with the expected redundancy rates of order O(log n) recently reported by various authors. Our approach is based on showing that...
Markov Types and Minimax Redundancy for Markov Sources
 IEEE Trans. Information Theory
, 2003
"... Redundancy of universal codes for a class of sources determines by how much the actual code length exceeds the optimal code length. In the minimax scenario one designs the best code for the worst source within the class. Such minimax redundancy comes in two flavors: either on average or for individu ..."
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Cited by 18 (10 self)
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Redundancy of universal codes for a class of sources determines by how much the actual code length exceeds the optimal code length. In the minimax scenario one designs the best code for the worst source within the class. Such minimax redundancy comes in two flavors: either on average or for individual sequences. The latter is also known as the maximal or the worst case minimax redundancy. We study the maximal minimax redundancy of universal block codes for Markovian sources of any order. We prove that the maximal minimax redundancy for Markov sources of order r is asymptotically equal to 1) log 2 n + log 2 A (ln ln m 1/(m1) )/ ln m + o(1), where n is the length of a source sequence, m is the size of the alphabet and A m is an explicit constant (e.g., we find that for a binary alphabet m = 2 and Markov of order r = 1 the constant 14.655449504 where G is the Catalan number). Unlike previous attempts, we view the redundancy problem as an asymptotic evaluation of certain sums over a set of matrices representing Markov types. The enumeration of Markov types is accomplished by reducing it to counting Eulerian paths in a multigraph. In particular, we propose an asymptotic formula for the number of strings of a given Markov type. All of these findings are obtained by analytic and combinatorial tools of analysis of algorithms. Index terms: Minimax redundancy, Markov sources, Markov types, Eulerian paths, multidimensional generating functions, analytic information theory. # A preliminary version of this paper was presented at Colloquium on Mathematics and Computer Science: Algorithms, Trees, Combinatorics and Probabilities, Versailles, 2002.
Secondorder noiseless source coding theorems
 IEEE TRANS. INFORM. THEORY
, 1997
"... Shannon’s celebrated source coding theorem can be viewed as a “onesided law of large numbers.” We formulate secondorder noiseless source coding theorems for the deviation of the codeword lengths from the entropy. For a class of sources that includes Markov chains we prove a “onesided central limi ..."
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Cited by 17 (7 self)
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Shannon’s celebrated source coding theorem can be viewed as a “onesided law of large numbers.” We formulate secondorder noiseless source coding theorems for the deviation of the codeword lengths from the entropy. For a class of sources that includes Markov chains we prove a “onesided central limit theorem” and a law of the iterated logarithm.
A MonteCarlo AIXI Approximation
, 2009
"... This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two key ways: First, the class of environment models is restricted to all prediction suffix trees of a fixed maximum depth. Thi ..."
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Cited by 11 (5 self)
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This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two key ways: First, the class of environment models is restricted to all prediction suffix trees of a fixed maximum depth. This allows a Bayesian mixture of environment models to be computed in time proportional to the logarithm of the size of the model class. Secondly, the finitehorizon expectimax search is approximated by an asymptotically convergent Monte Carlo Tree Search technique. This scaled down AIXI agent is empirically shown to be effective on a wide class of toy problem domains, ranging from simple fully observable games to small POMDPs. We explore the limits of this approximate agent and propose a general heuristic framework for scaling this technique to much larger problems.