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    ML
    2015
    ACM
    28views Machine Learning» more  ML 2015»
    Unsupervised ensemble minority clustering
    8 years 8 months ago
    Unsupervised ensemble minority clustering
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    Cluster analysis lies at the core of most unsupervised learning tasks. However, the majority of clustering algorithms depend on the all-in assumption, in which all objects belong ...
    Edgar González, Jordi Turmo
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    ML
    2015
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    6views Machine Learning» more  ML 2015»
    Learning relational dependency networks in hybrid domains
    8 years 8 months ago
    Learning relational dependency networks in hybrid domains
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    Irma Ravkic, Jan Ramon, Jesse Davis
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    ML
    2015
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    11views Machine Learning» more  ML 2015»
    Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions
    8 years 8 months ago
    Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions
    Download  researchcommons.waikato.ac.nz
    We prove theoretical guarantees for an averaging-ensemble of randomly projected Fisher Linear Discriminant classifiers, focusing on the case when there are fewer training observat...
    Robert J. Durrant, Ata Kabán
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    ML
    2015
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    8views Machine Learning» more  ML 2015»
    Soft-max boosting
    8 years 8 months ago
    Soft-max boosting
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    The standard multi-class classification risk, based on the binary loss, is rarely directly minimized. This is due to (i) the lack of convexity and (ii) the lack of smoothness (and...
    Matthieu Geist
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    2015
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    A framework to uncover multiple alternative clusterings
    8 years 8 months ago
    A framework to uncover multiple alternative clusterings
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    Xuan Hong Dang, James Bailey
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    ML
    2015
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    22views Machine Learning» more  ML 2015»
    Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine le
    8 years 8 months ago
    Linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine le
    Download  jmlr.org
    Many problems in statistics and machine learning (e.g., probabilistic graphical model, feature extraction, clustering and classification, etc) can be (re)formulated as linearly c...
    Zhouchen Lin, Risheng Liu, Huan Li
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    ML
    2015
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    11views Machine Learning» more  ML 2015»
    Probabilistic (logic) programming concepts
    8 years 8 months ago
    Probabilistic (logic) programming concepts
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    Abstract A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex...
    Luc De Raedt, Angelika Kimmig
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    ML
    2015
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    19views Machine Learning» more  ML 2015»
    A Bayesian approach for comparing cross-validated algorithms on multiple data sets
    8 years 8 months ago
    A Bayesian approach for comparing cross-validated algorithms on multiple data sets
    Download  people.idsia.ch
    We present a Bayesian approach for making statistical inference about the accuracy (or any other score) of two competing algorithms which have been assessed via cross-validation on...
    Giorgio Corani, Alessio Benavoli
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    2015
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    25views Machine Learning» more  ML 2015»
    Additive regularization of topic models
    8 years 8 months ago
    Additive regularization of topic models
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    Abstract. Probabilistic topic modeling of text collections is a powerful tool for statistical text analysis. Determining the optimal number of topics remains a challenging problem ...
    Konstantin Vorontsov, Anna Potapenko
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    ML
    2015
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    16views Machine Learning» more  ML 2015»
    Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
    8 years 8 months ago
    Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
    Download  spiral.imperial.ac.uk
    In recent years Predicate Invention has been underexplored within Inductive Logic Programming due to difficulties in formulating efficient search mechanisms. However, a recent p...
    Stephen H. Muggleton, Dianhuan Lin, Alireza Tamadd...
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