Discriminative, generative, and imitative learning
Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2005
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| Online Access: | http://hdl.handle.net/1721.1/8323 |
| _version_ | 1826204513164328960 |
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| author | Jebara, Tony (Tony S.), 1974- |
| author2 | Alex P. Pentland. |
| author_facet | Alex P. Pentland. Jebara, Tony (Tony S.), 1974- |
| author_sort | Jebara, Tony (Tony S.), 1974- |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002. |
| first_indexed | 2024-09-23T12:56:28Z |
| format | Thesis |
| id | mit-1721.1/8323 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:56:28Z |
| publishDate | 2005 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/83232019-04-11T00:26:55Z Discriminative, generative, and imitative learning Jebara, Tony (Tony S.), 1974- Alex P. Pentland. Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences. Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences. Architecture. Program in Media Arts and Sciences. Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2002. Includes bibliographical references (leaves 201-212). I propose a common framework that combines three different paradigms in machine learning: generative, discriminative and imitative learning. A generative probabilistic distribution is a principled way to model many machine learning and machine perception problems. Therein, one provides domain specific knowledge in terms of structure and parameter priors over the joint space of variables. Bayesian networks and Bayesian statistics provide a rich and flexible language for specifying this knowledge and subsequently refining it with data and observations. The final result is a distribution that is a good generator of novel exemplars. Conversely, discriminative algorithms adjust a possibly non-distributional model to data optimizing for a specific task, such as classification or prediction. This typically leads to superior performance yet compromises the flexibility of generative modeling. I present Maximum Entropy Discrimination (MED) as a framework to combine both discriminative estimation and generative probability densities. Calculations involve distributions over parameters, margins, and priors and are provably and uniquely solvable for the exponential family. Extensions include regression, feature selection, and transduction. SVMs are also naturally subsumed and can be augmented with, for example, feature selection, to obtain substantial improvements. To extend to mixtures of exponential families, I derive a discriminative variant of the Expectation-Maximization (EM) algorithm for latent discriminative learning (or latent MED). (cont.) While EM and Jensen lower bound log-likelihood, a dual upper bound is made possible via a novel reverse-Jensen inequality. The variational upper bound on latent log-likelihood has the same form as EM bounds, is computable efficiently and is globally guaranteed. It permits powerful discriminative learning with the wide range of contemporary probabilistic mixture models (mixtures of Gaussians, mixtures of multinomials and hidden Markov models). We provide empirical results on standardized data sets that demonstrate the viability of the hybrid discriminative-generative approaches of MED and reverse-Jensen bounds over state of the art discriminative techniques or generative approaches. Subsequently, imitative learning is presented as another variation on generative modeling which also learns from exemplars from an observed data source. However, the distinction is that the generative model is an agent that is interacting in a much more complex surrounding external world. It is not efficient to model the aggregate space in a generative setting. I demonstrate that imitative learning (under appropriate conditions) can be adequately addressed as a discriminative prediction task which outperforms the usual generative approach. This discriminative-imitative learning approach is applied with a generative perceptual system to synthesize a real-time agent that learns to engage in social interactive behavior. by Tony Jebara. Ph.D. 2005-08-23T19:11:37Z 2005-08-23T19:11:37Z 2002 2002 Thesis http://hdl.handle.net/1721.1/8323 50490135 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 212 leaves 21629727 bytes 21629483 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology |
| spellingShingle | Architecture. Program in Media Arts and Sciences. Jebara, Tony (Tony S.), 1974- Discriminative, generative, and imitative learning |
| title | Discriminative, generative, and imitative learning |
| title_full | Discriminative, generative, and imitative learning |
| title_fullStr | Discriminative, generative, and imitative learning |
| title_full_unstemmed | Discriminative, generative, and imitative learning |
| title_short | Discriminative, generative, and imitative learning |
| title_sort | discriminative generative and imitative learning |
| topic | Architecture. Program in Media Arts and Sciences. |
| url | http://hdl.handle.net/1721.1/8323 |
| work_keys_str_mv | AT jebaratonytonys1974 discriminativegenerativeandimitativelearning |