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38
Optimizing Search Engines using Clickthrough Data
, 2002
"... This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches ..."
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Cited by 1250 (23 self)
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This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the querylog of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be wellfounded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a metasearch engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.
On the algorithmic implementation of multiclass kernelbased vector machines
 Journal of Machine Learning Research
"... In this paper we describe the algorithmic implementation of multiclass kernelbased vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic ob ..."
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Cited by 547 (14 self)
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In this paper we describe the algorithmic implementation of multiclass kernelbased vector machines. Our starting point is a generalized notion of the margin to multiclass problems. Using this notion we cast multiclass categorization problems as a constrained optimization problem with a quadratic objective function. Unlike most of previous approaches which typically decompose a multiclass problem into multiple independent binary classification tasks, our notion of margin yields a direct method for training multiclass predictors. By using the dual of the optimization problem we are able to incorporate kernels with a compact set of constraints and decompose the dual problem into multiple optimization problems of reduced size. We describe an efficient fixedpoint algorithm for solving the reduced optimization problems and prove its convergence. We then discuss technical details that yield significant running time improvements for large datasets. Finally, we describe various experiments with our approach comparing it to previously studied kernelbased methods. Our experiments indicate that for multiclass problems we attain stateoftheart accuracy.
Discriminative Reranking for Natural Language Parsing
, 2005
"... This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this i ..."
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Cited by 327 (9 self)
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This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the loglikelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. The new model achieved 89.75 % Fmeasure, a 13 % relative decrease in Fmeasure error over the baseline model’s score of 88.2%. The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods within loglinear (maximumentropy) models. Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.
Automatic Construction of Decision Trees from Data: A MultiDisciplinary Survey
 Data Mining and Knowledge Discovery
, 1997
"... Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial ne ..."
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Cited by 222 (1 self)
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Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art. Keywords: classification, treestructured classifiers, data compaction 1. Introduction Advances in data collection methods, storage and processing technology are providing a unique challenge and opportunity for automated data exploration techniques. Enormous amounts of data are being collected daily from major scientific projects e.g., Human Genome...
Learning to resolve natural language ambiguities: A unified approach
 In Proceedings of the National Conference on Artificial Intelligence. 806813. Segond F., Schiller A., Grefenstette & Chanod F.P
, 1998
"... distinct semanticonceptsuch as interest rate and has interest in Math are conflated in ordinary text. We analyze a few of the commonly used statistics based The surrounding context word associations and synand machine learning algorithms for natural language tactic patterns in this case are suffl ..."
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Cited by 175 (79 self)
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distinct semanticonceptsuch as interest rate and has interest in Math are conflated in ordinary text. We analyze a few of the commonly used statistics based The surrounding context word associations and synand machine learning algorithms for natural language tactic patterns in this case are sufflcicnt to identify disambiguation tasks and observe tha they can bc recast as learning linear separators in the feature space. the correct form. Each of the methods makes a priori assumptions, which Many of these arc important standalone problems it employs, given the data, when searching for its hy but even more important is thei role in many applicapothesis. Nevertheless, as we show, it searches a space tions including speech recognition, machine translation, that is as rich as the space of all linear separators. information extraction and intelligent humanmachine We use this to build an argument for a data driven interaction. Most of the ambiguity resolution problems approach which merely searches for a good linear sepa are at the lower level of the natural language inferences rator in the feature space, without further assumptions chain; a wide range and a large number of ambigui
The Hardness of Approximate Optima in Lattices, Codes, and Systems of Linear Equations
, 1993
"... We prove the following about the Nearest Lattice Vector Problem (in any `p norm), the Nearest Codeword Problem for binary codes, the problem of learning a halfspace in the presence of errors, and some other problems. 1. Approximating the optimum within any constant factor is NPhard. 2. If for some ..."
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Cited by 173 (8 self)
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We prove the following about the Nearest Lattice Vector Problem (in any `p norm), the Nearest Codeword Problem for binary codes, the problem of learning a halfspace in the presence of errors, and some other problems. 1. Approximating the optimum within any constant factor is NPhard. 2. If for some ffl ? 0 there exists a polynomialtime algorithm that approximates the optimum within a factor of 2 log 0:5\Gammaffl n , then every NP language can be decided in quasipolynomial deterministic time, i.e., NP ` DTIME(n poly(log n) ). Moreover, we show that result 2 also holds for the Shortest Lattice Vector Problem in the `1 norm. Also, for some of these problems we can prove the same result as above, but for a larger factor such as 2 log 1\Gammaffl n or n ffl . Improving the factor 2 log 0:5\Gammaffl n to p dimension for either of the lattice problems would imply the hardness of the Shortest Vector Problem in `2 norm; an old open problem. Our proofs use reductions from fewpr...
Theory and Applications of Agnostic PACLearning with Small Decision Trees
, 1995
"... We exhibit a theoretically founded algorithm T2 for agnostic PAClearning of decision trees of at most 2 levels, whose computation time is almost linear in the size of the training set. We evaluate the performance of this learning algorithm T2 on 15 common "realworld" datasets, and show t ..."
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Cited by 83 (3 self)
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We exhibit a theoretically founded algorithm T2 for agnostic PAClearning of decision trees of at most 2 levels, whose computation time is almost linear in the size of the training set. We evaluate the performance of this learning algorithm T2 on 15 common "realworld" datasets, and show that for most of these datasets T2 provides simple decision trees with little or no loss in predictive power (compared with C4.5). In fact, for datasets with continuous attributes its error rate tends to be lower than that of C4.5. To the best of our knowledge this is the first time that a PAClearning algorithm is shown to be applicable to "realworld" classification problems. Since one can prove that T2 is an agnostic PAClearning algorithm, T2 is guaranteed to produce close to optimal 2level decision trees from sufficiently large training sets for any (!) distribution of data. In this regard T2 differs strongly from all other learning algorithms that are considered in applied machine learning, for w...
Learning in natural language
 Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI ’99); 31 July–6
, 1999
"... Statisticsbased classifiers in natural language are developed typically by assuming a generative model for the data, estimating its parameters from training data and then using Bayes rule to obtain a classifier. For many problems the assumptions made by the generative models are evidently wrong, le ..."
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Cited by 49 (23 self)
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Statisticsbased classifiers in natural language are developed typically by assuming a generative model for the data, estimating its parameters from training data and then using Bayes rule to obtain a classifier. For many problems the assumptions made by the generative models are evidently wrong, leaving open the question of why these approaches work. This paper presents a learning theory account of the major statistical approaches to learning in natural language. A class of Linear Statistical Queries (LSQ) hypotheses is defined and learning with it is shown to exhibit some robustness properties. Many statistical learners used in natural language, including naive Bayes, Markov Models and Maximum Entropy models are shown to be LSQ hypotheses, explaining the robustness of these predictors even when the underlying probabilistic assumptions do not hold. This coherent view of when and why learning approaches work in this context may help to develop better learning methods and an understanding of the role of learning in natural language inferences. 1
Computing the maximum bichromatic discrepancy, with applications to computer graphics and machine learning
 J. Computer and Systems Sciences
, 1996
"... Computing the maximum bichromatic discrepancy is an interesting theoretical problem with important applications in computational learning theory, computational geometry and computer graphics. In this paper we give algorithms to compute the maximum bichromatic discrepancy for simple geometric ranges, ..."
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Cited by 41 (8 self)
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Computing the maximum bichromatic discrepancy is an interesting theoretical problem with important applications in computational learning theory, computational geometry and computer graphics. In this paper we give algorithms to compute the maximum bichromatic discrepancy for simple geometric ranges, including rectangles and halfspaces. In addition, we give extensions to other discrepancy problems.] 1996 Academic Press, Inc. 1.
Efficient agnostic paclearning with simple hypotheses
 Proc. of the 7th Annual ACM Conference on Computational Learning Theory
, 1994
"... We exhibit efficient algorithms for agnostic PAClearning with rectangles, unions of two rectangles, and unions of k intervals as hypotheses. These hypothesis classes are of some interest from the point of view of applied machine learning, because empirical studies show that hypotheses of this simp ..."
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Cited by 39 (3 self)
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We exhibit efficient algorithms for agnostic PAClearning with rectangles, unions of two rectangles, and unions of k intervals as hypotheses. These hypothesis classes are of some interest from the point of view of applied machine learning, because empirical studies show that hypotheses of this simple type (in just one or two of the attributes) provide good prediction rules for various realworld classification problems. In addition, optimal hypotheses of this type may provide valuable heuristic insight into the structure of a realworld classification problem, The algorithms that are introduced in this paper make it feasible to compute optimal hypotheses of this type for a training set of several hundred examples. We also exhibit an approximation algorithm that can compute nearly optimal hypotheses for much larger datasets.