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Pattern Recognition Letters, ISSN 0167-8655, 09/2018, Volume 112, pp. 131 - 137
.... Recent work has shown substantial performance improvements of discriminative probabilistic models over their generative counterparts... 
Large margin learning | Missing features | Hybrid generative-discriminative learning | Semi-supervised learning | Gaussian mixture model | CLASSIFICATION | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Journal Article
Pattern Recognition, ISSN 0031-3203, 2012, Volume 45, Issue 4, pp. 1326 - 1340
Journal Article
Pattern Analysis and Applications, ISSN 1433-7541, 8/2013, Volume 16, Issue 3, pp. 349 - 363
Journal Article
Optical Engineering, ISSN 0091-3286, 02/2011, Volume 50, Issue 2, pp. 027203 - 027203
.... Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting... 
hybrid generative-discriminative learning | supervised topic models | spatiotemporal words | action categorization | OPTICS | CATEGORIES | Learning | Human | Allocations | Algorithms | Data sets | Dirichlet problem | Mathematical models | Recognition
Journal Article
Applied intelligence (Dordrecht, Netherlands), ISSN 1573-7497, 2019, Volume 49, Issue 11, pp. 3783 - 3800
Journal Article
IEEE access, ISSN 2169-3536, 2019, Volume 7, pp. 1107 - 1117
This paper aims to propose a robust hybrid probabilistic learning approach that combines appropriately the advantages of both the generative and discriminative models for the challenging problem... 
Retinal images | Retinopathy | SVM | probabilistic kernels | Support vector machines | MDL | scaled Dirichlet mixture | generative-discriminative learning | Mixture models | Feature extraction | Data models | Diabetes | Kernel | DIABETIC-RETINOPATHY | SYSTEM | AUTOMATIC DETECTION | COMPUTER SCIENCE, INFORMATION SYSTEMS | TELECOMMUNICATIONS | BLOOD-VESSEL SEGMENTATION | ENGINEERING, ELECTRICAL & ELECTRONIC | LESION DETECTION | MICROANEURYSM DETECTION | SELECTION
Journal Article
IEEE Transactions on Pattern Analysis and Machine Intelligence, ISSN 0162-8828, 01/2018, Volume 40, Issue 1, pp. 106 - 118
Journal Article
2019 IEEE International Symposium on Multimedia (ISM), 12/2019, pp. 231 - 2311
...). We propose the use of Fisher kernels with Generalized Dirichlet based hidden Markov models (HMM) for DT recognition... 
Fisher Kernels | Generalized Dirichlet | Hybrid-Generative Discriminative | Hidden Markov Models | Dynamic Textures | Proportional Data | Support Vector Machines
Conference Proceeding
2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), 11/2019, pp. 1 - 5
.... In this paper, we propose the use of Fisher kernels with Dirichlet based and Beta-Liouville (BL) based hidden Markov models... 
Generative-Discriminative | Fisher Kernels | Hidden Markov Models | Dynamic Textures | SVM | Proportional Data
Conference Proceeding
Neurocomputing, ISSN 0925-2312, 2009, Volume 72, Issue 7, pp. 1648 - 1655
... algorithm proposed in Raina et al. [Classification with hybrid generative/discriminative models, in: NIPS, 2003... 
Statistical modelling and learning | Probabilistic generative and discriminative approaches | Hybrid generative/discriminative models | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Analysis | Models | Algorithms
Journal Article
Proceedings of SPIE - The International Society for Optical Engineering, ISSN 0277-786X, 2007, Volume 6560
Conference Proceeding
2019 IEEE Milan PowerTech, 06/2019, pp. 1 - 5
...), where the generator is formed by modelling data with a Gaussian mixture model and provides the estimated probability distribution function (pdf... 
Voltage fluctuations | Voltage measurement | Generators | Deep Active Learning | Gallium nitride | Automatic Labelling | Deep Learning | Support vector machines | Semi-supervised Training | Voltage Dip | Generative adversarial networks | Labeling | Generative-Discriminative Model
Conference Proceeding
Neurocomputing, ISSN 0925-2312, 10/2016, Volume 208, pp. 218 - 224
Journal Article
2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), 11/2011, pp. 922 - 924
.... In this paper, we propose a computationally efficient method for nucleosome positioning. By using generative/discriminative models, the proposed methods can categorize... 
Support vector machines | Generative/Discriminative Models | Biological system modeling | Computational modeling | DNA | Genomics | Support Vector Machine | Bioinformatics | Principal component analysis | Principal Component Analysis
Conference Proceeding
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