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检索条件"机构=RSISE Statistical Machine Learning Program National ICT Australia"
11 条 记 录,以下是1-10 订阅
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Feature selection via dependence maximization
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The Journal of machine learning Research 2012年 第1期13卷
作者: Le Song Alex Smola Arthur Gretton Justin Bedo Karsten Borgwardt Computational Science and Engineering Georgia Institute of Technology Atlanta GA Yahoo! Research Santa Clara CA Gatsby Computational Neuroscience Unit London UK and Intelligent Systems Group Max Planck Institutes Tübingen Germany Statistical Machine Learning Program National ICT Australia Canberra ACT Australia and Australian National University Canberra ACT Australia Machine Learning and Computational Biology Research Group Max Planck Institutes Tübingen Germany
We introduce a framework for feature selection based on dependence maximization between the selected features and the labels of an estimation problem, using the Hilbert-Schmidt Independence Criterion. The key idea is ... 详细信息
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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...
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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 l...
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Hyperparameter learning for graph based semi-supervised learning algorithms  19
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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
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 algorit... 详细信息
来源: 评论
Simpler knowledge-based support vector machines  06
Simpler knowledge-based support vector machines
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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
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 p... 详细信息
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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...
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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
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. M... 详细信息
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Hyperparameter learning for graph based semi-supervised learning algorithms  06
Hyperparameter learning for graph based semi-supervised lear...
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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 algorit...
来源: 评论
Learnability of probabilistic automata via oracles
Learnability of probabilistic automata via oracles
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16th International Conference on Algorithmic learning Theory, ALT 2005
作者: Guttman, Omri Vishwanathan, S.V.N. Williamson, Robert C. Statistical Machine Learning Program National ICT Australia Australian National University Canberra ACT Australia
Efficient learnability using the state merging algorithm is known for a subclass of probabilistic automata termed μ-distinguishable. In this paper, we prove that state merging algorithms can be extended to efficientl... 详细信息
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Heteroscedastic Gaussian process regression  05
Heteroscedastic Gaussian process regression
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ICML 2005: 22nd International Conference on machine learning
作者: Le, Quoc V. Smola, Alex J. Canu, Stéphane RSISE Australian National University ACT 0200 Australia Statistical Machine Learning Program National ICT Australia ACT 0200 Australia PSI - FRE CNRS 2645 INSA de Rouen France
This paper presents an algorithm to estimate simultaneously both mean and variance of a non parametric regression problem. The key point is that we are able to estimate variance locally unlike standard Gaussian Proces... 详细信息
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Step size-adapted online support vector learning
Step size-adapted online support vector learning
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8th International Symposium on Signal Processing and its Applications, ISSPA 2005
作者: Karatzoglou, Alexandros Vishwanathan, S.V.N. Schraudolph, Nicol N. Smola, Alex J. Department of Statistics Technische Universität Wien Wiedner Hauptstraße 8-10 Austria National ICT Australia Statistical Machine Learning Program Australian National University Canberra
We present an online Support Vector machine (SVM) that uses Stochastic Meta-Descent (SMD) to adapt its step size automatically. We formulate the online learning problem as a stochastic gradient descent in Reproducing ... 详细信息
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Step size-adapted online support vector learning
Step size-adapted online support vector learning
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International Symposium on Signal Processing and Its Applications (ISSPA)
作者: A. Karatzoglou S.V.N. Vishwanathan N.N. Schraudolph A.J. Smola Department of Statistics Technische Universität Wien Austria RSISE Statistical Machine Learning Program National ICT Australia Australian National University Canberra Australia
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