Hybrid Generative-Discriminative Models
Classification with Hybrid Generative/Discriminative Models
... limited, generative classifiers can out-perform ...a hybrid model in which a high-dimensional subset of the parameters are trained to maximize generative likelihood, and another, small, subset of ...
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Exploiting Domain Structure with Hybrid Generative-Discriminative Models
... a hybrid between logistic regression and naive Bayes and showed that it can obtain higher accuracy than baseline methods when an overlapping set structure is present in the ...
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Hybrid generative-discriminative training of Gaussian mixture models
... of discriminative probabilistic mod- els over their generative ...since discriminative models do not capture the input distribution of the data, their use in missing data scenarios is ...
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A Hybrid Generative/Discriminative Approach To Citation Prediction
... It was expected that when run on the entire ACL corpus, WSIC and our Logic-Expanded systems would have sufficient data to learn authors’ citing preferences and would outperform the other genera- tive models. As ...
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Hybrid Discriminative-Generative Approach with Gaussian Processes
... In each case, the number of inducing inputs used was the same for both models. The covariance functions were all taken to be an exponentiated quadratic with white noise. The values in the table correspond to the ...
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Scene Classification Using a Hybrid Generative/Discriminative Approach
... We compare our classification performance to that of four previous methods [9], [18], [24], [32] using the authors’ own databases. This previous work uses varying levels of supervision in training (compared to the ...
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A Hybrid Discriminative/Generative Approach for Modeling Human Activities
... a hybrid approach to recognizing activities, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov ...
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Integration of discriminative and generative models for activity recognition in smart homes
... hand, generative approaches improve the generalization ability by modeling the underlying distribution of classes from the obtained feature ...space. Generative models are flexible, since they learn ...
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Aspects of generative and discriminative classifiers
... compare generative and discriminative classifiers and then combine ...two hybrid-learning techniques, namely the hybrid generative-discriminative algorithm (Raina et ...the ...
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Semi Supervised Structured Output Learning Based on a Hybrid Generative and Discriminative Approach
... a hybrid generative and discriminative approach. A hybrid approach was first proposed in a super- vised learning setting (Raina et ...with generative mod- els that incorporate unlabeled ...
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A Hybrid Generative/Discriminative Framework to Train a Semantic Parser from an Un annotated Corpus
... a hybrid genera- tive/discriminative framework for se- mantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HM- ...Markov models and the ...
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Efficient Integration of Generative Topic Models Into Discriminative Classifiers Using Robust Probabilistic Kernels
... topic models. These characteristics il- lustrate the e↵ectiveness of our hybrid models and their performance within a wide variety of datasets show- ing therefore the ability for our proposed ...
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Hybrids of Generative and Discriminative Methods for Machine Learning
... of hybrid models from a theoretical point of ...the generative model has asymptotically the best classification performance while, if wrong model assumptions are made, a hybrid model should ...
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The generative learning and discriminative fitting of linear deformable models
... cal models of shape and appearance to represent a deformable visual ...statistical models are simultaneously LDM’s greatest strength and ...statistical models of shape and ...
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Refining Generative Language Models using Discriminative Learning
... Our method bears some similarity to the recently developed Contrastive Estimation method (Smith and Eisner, 2004). Contrastive estimation (CE) was proposed as a means for training log-linear prob- abilistic ...
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Generative and discriminative models for person verification and efficient search
... Research Models discussed in previous chapters seem to have resolved most issues in prac- tical person verification ...simple models, for instance, Gaussian assumptions in both CVM and MCVM, and ...
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Integration of discriminative and generative models for activity recognition in smart homes
... Copyright and reuse: City Research Online aims to make research outputs of City, University of London available to a wider audience.. Copyright and Moral Rights remain with the author([r] ...
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Comparison of Bayesian Discriminative and Generative Models for Dialogue State Tracking
... The transition model describes the probability that the user will change his/her goal, given the previous goal and the last system action. For ex- ample, if the system asks the user about a specific slot, then it is ...
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IRGAN: a minimax game for unifying generative and discriminative information retrieval models
... the generative model, acting as an attacker to the current discriminative model, generates difficult examples for the discriminative model in an adversarial way by minimising its discrimination ...
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IRGAN: a minimax game for unifying generative and discriminative information retrieval models
... Discriminative Information Retrieval Models Anonymous Authors ABSTRACT This paper provides a unified account of two schools of thinking in information retrieval modelling: the generative retrieval ...
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