Comparison between Typical Discriminative Learning Model and Generative Model in Chinese Short Messages Service Spam Filtering

2010 ◽  
Author(s):  
Xiaoxia Zheng ◽  
Chao Liu ◽  
Chengzhe Huang ◽  
Yu Zou ◽  
Hongwei Yu
Keyword(s):  
Learning Model ◽  
Generative Model ◽  
Discriminative Learning ◽  
Spam Filtering ◽  
Short Messages

A Fast Learning Algorithm for Deep Belief Nets

2006 ◽  
Vol 18 (7) ◽  
pp. 1527-1554 ◽  
Author(s):  
Geoffrey E. Hinton ◽  
Simon Osindero ◽  
Yee-Whye Teh
Keyword(s):  
Greedy Algorithm ◽  
Associative Memory ◽  
Learning Algorithm ◽  
Generative Model ◽  
Fine Tuning ◽  
Discriminative Learning ◽  
Fast Learning ◽  
Learning Procedure ◽  
Handwritten Digit ◽  
Low Dimensional

We show how to use “complementary priors” to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.

Spam filtering for short messages in adversarial environment

2015 ◽  
Vol 155 ◽  
pp. 167-176 ◽  
Author(s):  
Patrick P.K. Chan ◽  
Cheng Yang ◽  
Daniel S. Yeung ◽  
Wing W.Y. Ng
Keyword(s):  
Spam Filtering ◽  
Short Messages

scholarly journals Réseaux bayésiens jumelés et noyau de Fisher pondéré pour la classification de documents XML

2014 ◽  
Vol Volume 17 - 2014 - Special... ◽  
Author(s):  
Ait Ali Yahia Yassine ◽  
Amrouche Karima
Keyword(s):  
Response Time ◽  
Bayesian Networks ◽  
Coupled Model ◽  
Learning Model ◽  
Document Classification ◽  
Generative Model ◽  
Xml Document ◽  
International Audience

International audience In this paper, we are presenting a learning model for XML document classification based on Bayesian networks. Then, we are proposing a model which simplifies the arborescent representation of the XML document that we have, named coupled model and we will see that this approach improves the response time and keeps the same performances of the classification. Then, we will study an extension of this generative model to the discriminating model thanks to the formalism of the Fisher’s kernel. At last, we have applied a ponderation of the structure components of the Fisher’s vector. We finish by presenting the obtained results on the XML collection by using the CBS and SVM methods

scholarly journals Short Messages Spam Filtering Using Sentiment Analysis

2016 ◽  
pp. 142-153
Author(s):  
Enaitz Ezpeleta ◽  
Urko Zurutuza ◽  
José María Gómez Hidalgo
Keyword(s):  
Sentiment Analysis ◽  
Spam Filtering ◽  
Short Messages
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