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Using two-stage conditional word frequency models to model word burstiness and motivating TF-IDF

Using two-stage conditional word frequency models to model w...

引用

11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007

作者： Sunehag, Peter Statistical Machine Learning Program National ICT Australia Locked bag 8001 ACT 2601 Australia

Several authors have recently studied the problem of creating exchangeable models for natural languages that exhibit word burstiness. Word burstiness means that a word that has appeared once in a text should be more likely to appear again than it was to appear in the first place. In this article the different existing methods are compared theoretically through a unifying framework. New models that do not satisfy the exchangeability assumption but whose probability revisions only depend on the word counts of what has previously appeared, are introduced within this framework. We will refer to these models as two-stage conditional presence/ abundance models since they, just like some recently introduced models for the abundance of rare species in ecology, seperate the issue of presence from the issue of abundance when present. We will see that the widely used TF-IDF heuristic for information retrieval follows naturally from these models by calculating a cross-entropy. We will also discuss a connection between TF-IDF and file formats that seperate presence from abundance given presence.

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learning to rank with nonsmooth cost functions

引用

20th Annual Conference on Neural Information Processing Systems, NIPS 2006

作者： Burges, Christopher J.C. Ragno, Robert Viet Le, Quoc Microsoft Research One Microsoft Way Redmond WA 98052 United States Statistical Machine Learning Program NICTA ACT 2601 Australia

ISBN: (纸本)9780262195683

The quality measures used in information retrieval are particularly difficult to optimize directly, since they depend on the model scores only through the sorted order of the documents returned for a given query. Thus, the derivatives of the cost with respect to the model parameters are either zero, or are undefined. In this paper, we propose a class of simple, flexible algorithms, called LambdaRank, which avoids these difficulties by working with implicit cost functions. We describe LambdaRank using neural network models, although the idea applies to any differentiable function class. We give necessary and sufficient conditions for the resulting implicit cost function to be convex, and we show that the general method has a simple mechanical interpretation. We demonstrate significantly improved accuracy, over a state-of-the-art ranking algorithm, on several datasets. We also show that LambdaRank provides a method for significantly speeding up the training phase of that ranking algorithm. Although this paper is directed towards ranking, the proposed method can be extended to any non-smooth and multivariate cost functions.

关键词： Cost functions

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Hyperparameter learning for graph based semi-supervised learning algorithms 19

引用

20th Annual Conference on Neural Information Processing Systems, NIPS 2006

作者： Zhang, Xinhua Lee, Wee Sun Statistical Machine Learning Program National ICT Australia Canberra Australia CSL RSISE ANU Canberra Australia Department of Computer Science National University of Singapore 3 Science Drive 2 Singapore 117543 Singapore

ISBN: (纸本)9780262195683

Semi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorithms, for which the performance depends considerably on the quality of the graph, or its hyperparameters. In this paper, we deal with the less explored problem of learning the graphs. We propose a graph learning method for the harmonic energy minimization method;this is done by minimizing the leave-one-out prediction error on labeled data points. We use a gradient based method and designed an efficient algorithm which significantly accelerates the calculation of the gradient by applying the matrix inversion lemma and using careful pre-computation. Experimental results show that the graph learning method is effective in improving the performance of the classification algorithm.

关键词： learning algorithms

来源：评论

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Supervised feature selection via dependence estimation 07

Supervised feature selection via dependence estimation

引用

24th International Conference on machine learning, ICML 2007

作者： Song, Le Smola, Alex Gretton, Arthur Borgwardt, Karsten M. Bedo, Justin NICTA Statistical Machine Learning Program Canberra ACT 0200 Australia University of Sydney ANU MPI for Biological Cybernetics Spemannstr. 38 72076 Tübingen Germany LMU Department Institute for Computer Science Oettingenstr. 67 80538 München Germany

ISBN: (纸本)9781595937933

We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.

关键词： Feature extraction

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A dependence maximization view of clustering 07

A dependence maximization view of clustering

引用

24th International Conference on machine learning, ICML 2007

作者： Song, Le Smola, Alex Gretton, Arthur Borgwardt, Karsten M. NICTA Statistical Machine Learning Program Canberra ACT 0200 Australia University of Sydney ANU MPI for Biological Cybernetics Spemannstr. 38 72076 Tübingen Germany LMU Department Institute for Computer Science Oettingenstr. 67 80538 München Germany

ISBN: (纸本)9781595937933

We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Independence Criterion (HSIC). Under this framework, we unify the geometric, spectral, and statistical dependence views of clustering, and subsume many existing algorithms as special cases (e.g. k-means and spectral clustering). Distinctive to our framework is that kernels can also be applied on the labels, which can endow them with particular structures. We also obtain a perturbation bound on the change in k-means clustering.

关键词： Clustering algorithms

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learning to Rank with Nonsmooth Cost Functions 19

引用

19th International Conference on Neural Information Processing Systems, NIPS 2006

作者： Burges, Christopher J.C. Ragno, Robert Le, Quoc Viet Microsoft Research One Microsoft Way RedmondWA98052 United States Statistical Machine Learning Program NICTA ACT2601 Australia

ISBN: (纸本)0262195682

关键词： Cost functions

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Class prediction from time series gene expression profiles using dynamical systems kernels

Class prediction from time series gene expression profiles u...

引用

11th Pacific Symposium on Biocomputing 2006, PSB 2006

作者： Borgwardt, Karsten M. Vishwanathan, S.V.N. Kribgel, Hans-Peter Institute for Computer Science Ludwig-Maximilians-University Oettingenstr. 67 80538 Munich Germany Statistical Machine Learning Program National ICT Australia Canberra ACT 0200 Australia

ISBN: (纸本)9812564632

We present a kernel-based approach to the classification of time series of gene expression profiles. Our method takes into account the dynamic evolution over time as well as the temporal characteristics of the data. More specifically, we model the evolution of the gene expression profiles as a Linear Time Invariant (LTI) dynamical system and estimate its model parameters, A kernel on dynamical systems is then used to classify these time series. We successfully test our approach on a published dataset to predict response to drug therapy in Multiple Sclerosis patients. For phartnacogenomics, our method offers a huge potential for advanced computational tools in disease diagnosis, and disease and drug therapy outcome prognosis.

关键词： Dynamical systems

来源：评论

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Simpler knowledge-based support vector machines 06

Simpler knowledge-based support vector machines

引用

23rd International Conference on machine learning, ICML 2006

作者： Le, Quoc V. Smola, Alex J. Gärtner, Thomas RSISE Australian National University 0200 ACT Australia Statistical Machine Learning Program National ICT Australia 0200 ACT Australia Fraunhofer AIS.KD Schloß Birlinghoven 53754 Sankt Augustin Germany

ISBN: (纸本)1595933832

If appropriately used, prior knowledge can significantly improve the predictive accuracy of learning algorithms or reduce the amount of training data needed. In this paper we introduce a simple method to incorporate prior knowledge in support vector machines by modifying the hypothesis space rather than the optimization problem. The optimization problem is amenable to solution by the constrained concave convex procedure, which finds a local optimum. The paper discusses different kinds of prior knowledge and demonstrates the applicability of the approach in some characteristic experiments.

关键词： Support vector machines

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learning to rank with nonsmooth cost functions 06

Learning to rank with nonsmooth cost functions

引用

Proceedings of the 19th International Conference on Neural Information Processing Systems

作者： Christopher J. C. Burges Robert Ragno Quoc Viet Le Microsoft Research One Microsoft Way Redmond WA Statistical Machine Learning Program NICTA Australia

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Hyperparameter learning for graph based semi-supervised learning algorithms 06

Hyperparameter learning for graph based semi-supervised lear...

引用

Proceedings of the 20th International Conference on Neural Information Processing Systems

作者： Xinhua Zhang Wee Sun Lee Statistical Machine Learning Program National ICT Australia Canberra Australia and CSL RSISE ANU Canberra Australia Department of Computer Science National University of Singapore Singapore

Semi-supervised learning algorithms have been successfully applied in many applications with scarce labeled data, by utilizing the unlabeled data. One important category is graph based semi-supervised learning algorithms, for which the performance depends considerably on the quality of the graph, or its hyperparameters. In this paper, we deal with the less explored problem of learning the graphs. We propose a graph learning method for the harmonic energy minimization method; this is done by minimizing the leave-one-out prediction error on labeled data points. We use a gradient based method and designed an efficient algorithm which significantly accelerates the calculation of the gradient by applying the matrix inversion lemma and using careful pre-computation. Experimental results show that the graph learning method is effective in improving the performance of the classification algorithm.

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