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    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="http://arxiv.org/abs/1207.4139"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1206.6858"/>
	<rdf:li rdf:resource="http://jmlr.csail.mit.edu/papers/v9/lebanon08a.html"/>
	<rdf:li rdf:resource="http://jmlr.csail.mit.edu/papers/v11/dillon10a.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1003.0470"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1003.0691"/>
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    </items>
  </channel><item rdf:about="http://arxiv.org/abs/1207.4139">
    <title>[1207.4139] An Extended Cencov-Campbell Characterization of Conditional Information Geometry</title>
    <dc:date>2012-08-01T03:27:43+00:00</dc:date>
    <link>http://arxiv.org/abs/1207.4139</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We formulate and prove an axiomatic characterization of conditional information geometry, for both the normalized and the nonnormalized cases. This characterization extends the axiomatic derivation of the Fisher geometry by Cencov and Campbell to the cone of positive conditional models, and as a special case to the manifold of conditional distributions. Due to the close connection between the conditional I-divergence and the product Fisher information metric the characterization provides a new axiomatic interpretation of the primal problems underlying logistic regression and AdaBoost."]]></description>
<dc:subject>to:NB fisher_information information_geometry statistics boosting lebanon.guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d9ace99d20d7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fisher_information"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_geometry"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:boosting"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lebanon.guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1206.6858">
    <title>[1206.6858] Sequential Document Representations and Simplicial Curves</title>
    <dc:date>2012-07-12T17:25:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1206.6858</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The popular bag of words assumption represents a document as a histogram of word occurrences. While computationally efficient, such a representation is unable to maintain any sequential information. We present a continuous and differentiable sequential document representation that goes beyond the bag of words assumption, and yet is efficient and effective. This representation employs smooth curves in the multinomial simplex to account for sequential information. We discuss the representation and its geometric properties and demonstrate its applicability for the task of text classification."]]></description>
<dc:subject>lebanon.guy text_mining machine_learning data_mining to_teach:data-mining in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:173a644a5c38/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lebanon.guy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:text_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/papers/v9/lebanon08a.html">
    <title>Non-Parametric Modeling of Partially Ranked Data</title>
    <dc:date>2012-02-05T18:54:04+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/papers/v9/lebanon08a.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Statistical models on full and partial rankings of n items are often of limited practical use for large n due to computational consideration. We explore the use of non-parametric models for partially ranked data and derive computationally efficient procedures for their use for large n. The derivations are largely possible through combinatorial and algebraic manipulations based on the lattice of partial rankings. A bias-variance analysis and an experimental study demonstrate the applicability of the proposed method."]]></description>
<dc:subject>to:NB statistics machine_learning categorical_data ordinal_data information_retrieval nonparametrics lebanon.guy</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:44482224cc87/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:categorical_data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ordinal_data"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_retrieval"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lebanon.guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jmlr.csail.mit.edu/papers/v11/dillon10a.html">
    <title>Stochastic Composite Likelihood</title>
    <dc:date>2010-11-06T14:51:35+00:00</dc:date>
    <link>http://jmlr.csail.mit.edu/papers/v11/dillon10a.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. Each of the estimators resolve the computation-accuracy tradeoff differently, and taken together they span a continuous spectrum of computation-accuracy tradeoff resolutions. We prove the consistency of the estimators, provide formulas for their asymptotic variance, statistical robustness, and computational complexity. We discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators when the computational resources are insufficient. They also demonstrate that in some cases reduced computational complexity is associated with robustness thereby increasing statistical accuracy."
]]></description>
<dc:subject>likelihood estimation statistics to_read lebanon.guy</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b30b69e0a813/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:likelihood"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lebanon.guy"/>
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</item>
<item rdf:about="http://arxiv.org/abs/1003.0470">
    <title>[1003.0470] Unsupervised Supervised Learning II: Training Margin Based Classifiers without Labels</title>
    <dc:date>2010-03-04T16:55:48+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.0470</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing a margin-based risk function. Traditionally, these risk functions are computed based on a labeled dataset. We develop a novel technique for estimating such risks using only unlabeled data and p(y). We prove that the technique is consistent for high-dimensional linear classifiers and demonstrate it on synthetic and real-world data. In particular, we show how the estimate is used for evaluating classifiers in transfer learning, and for training classifiers with no labeled data whatsoever."  --- Now, this abstract makes absolutely no sense to me (I mean it, none whatsoever), but since Guy is the one saying these senseless things, I assume that they actually make sense somehow.
]]></description>
<dc:subject>semi-supervised_learning learning_theory classifiers re:naive-semi-supervised in_NB lebanon.guy</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c4e8236d543a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:semi-supervised_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:naive-semi-supervised"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lebanon.guy"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1003.0691">
    <title>[1003.0691] Statistical and Computational Tradeoffs in Stochastic Composite Likelihood</title>
    <dc:date>2010-03-04T13:25:08+00:00</dc:date>
    <link>http://arxiv.org/abs/1003.0691</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Maximum likelihood estimators are often of limited practical use due to the intensive computation they require. We propose a family of alternative estimators that maximize a stochastic variation of the composite likelihood function. Each of the estimators resolve the computation-accuracy tradeoff differently, and taken together they span a continuous spectrum of computation-accuracy tradeoff resolutions. We prove the consistency of the estimators, provide formulas for their asymptotic variance, statistical robustness, and computational complexity. We discuss experimental results in the context of Boltzmann machines and conditional random fields. The theoretical and experimental studies demonstrate the effectiveness of the estimators when the computational resources are insufficient. They also demonstrate that in some cases reduced computational complexity is associated with robustness thereby increasing statistical accuracy."
]]></description>
<dc:subject>statistics estimation likelihood computational_statistics lebanon.guy</dc:subject>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f2a6c1a92507/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:likelihood"/>
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