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            <title xml:lang="en">Online Learning for Two Novel Latent Topic Models</title>
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                <forename type="first">Ali</forename>
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                <editor>Gerhard Weikum</editor>
                <editor>Linawati</editor>
                <editor>Made Sudiana Mahendra</editor>
                <editor>Erich J. Neuhold</editor>
                <editor>A Min Tjoa</editor>
                <editor>Ilsun You</editor>
                <editor>Josef Kittler</editor>
                <editor>Jon M. Kleinberg</editor>
                <editor>Alfred Kobsa</editor>
                <editor>Friedemann Mattern</editor>
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                <editor>Moni Naor</editor>
                <editor>Oscar Nierstrasz</editor>
                <editor>C. Pandu Rangan</editor>
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                <term xml:lang="en">Latent model</term>
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              <p>Latent topic models have proven to be an efficient tool for modeling multitopic count data. One of the most well-known models is the latent Dirichlet allocation (LDA). In this paper we propose two improvements for LDA using generalized Dirichlet and Beta-Liouville prior assumptions. Moreover, we apply an online learning approach for both introduced approaches. We choose a challenging application namely natural scene classification for comparison and evaluation purposes.</p>
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