Exploiting Generative Models in Discriminative Classifiers
نویسندگان
چکیده
Generative probability models deal with missing information and variable length sequences.
منابع مشابه
Exploiting generative models discriminative classifiers • In
Generative probability models such as hidden ~larkov models provide a principled way of treating missing information and dealing with variable length sequences. On the other hand , discriminative methods such as support vector machines enable us to construct flexible decision boundaries and often result in classification performance superior to that of the model based approaches. An ideal class...
متن کاملA Logic-based Approach to Generatively Defined Discriminative Modeling
Conditional random fields (CRFs) are usually specified by graphical models but in this paper we propose to use probabilistic logic programs and specify them generatively. Our intension is first to provide a unified approach to CRFs for complex modeling through the use of a Turing complete language and second to offer a convenient way of realizing generative-discriminative pairs in machine learn...
متن کاملAugmented Statistical Models: Exploiting Generative Models in Discriminative Classifiers
In recent years, many algorithms have been proposed for discriminative classification of data. Popular examples are support vector machines (SVMs) [1] and conditional random fields (CRFs) [2]. These techniques make extensive use of fixed-dimensional mappings from the observation-space to a (often high-dimensional) feature-space. Unfortunately, for applications with variable-length sequences of ...
متن کاملAre Generative Classifiers More Robust to Adversarial Attacks?
There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed. However, most recent work focuses on discriminative classifiers which only models the conditional distribution of the labels given the inputs. In this abstract we propose deep Bayes classifier that improves the c...
متن کاملExploiting Unlabelled Data for Hybrid Object Classification
We propose a semi-supervised learning algorithm for visual object categorization which utilizes statistical information from unlabelled data to increase classification performance. We build on an earlier hybrid generative-discriminative approach by Holub et al. [6] which extracts Fisher scores from generative models. The hybrid model allows us to combine the modelling power and flexibility of g...
متن کامل