Book Chapter10.1007/978-3-642-22152-1_35
Latent tree classifier
Yi Wang,Nevin L. Zhang,Tao Chen,Leonard K. M. Poon +3 more
- 29 Jun 2011
- pp 410-421
5
TL;DR: This work proposes a novel generative model for classification called latent tree classifier (LTC), which represents each class-conditional distribution of attributes using a latent tree model, and uses Bayes rule to make prediction.
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Abstract: We propose a novel generative model for classification called latent tree classifier (LTC). An LTC represents each class-conditional distribution of attributes using a latent tree model, and uses Bayes rule to make prediction. Latent tree models can capture complex relationship among attributes. Therefore, LTC can approximate the true distribution behind data well and thus achieve good classification accuracy. We present an algorithm for learning LTC and empirically evaluate it on 37 UCI data sets. The results show that LTC compares favorably to the state-of-the-art. We also demonstrate that LTC can reveal underlying concepts and discover interesting subgroups within each class.
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Citations
A survey on latent tree models and applications
TL;DR: This review of the latent tree model, a particular type of probabilistic graphical models, deserves attention because its simple structure allows simple and efficient inference, while its latent variables capture complex relationships.
LTC: A latent tree approach to classification
TL;DR: This paper proposes a novel generative classifier called latent tree classifier (LTC), which represents each class-conditional distribution of attributes using a latent tree model, and uses Bayes rule to make prediction.
10
Semi-hierarchical naïve Bayes classifier
Hasna Njah,Salma Jamoussi,Walid Mahdi +2 more
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TL;DR: A new semi-hierarchical naïve Bayes that uses the latent variables for abstracting the features of a given dataset in order to reduce the dimensionality and is suitable for finding graphically and semantically analyzable models.
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•Dissertation
Accelerated learning of latent tree models for topic detection and multidimensional clustering
Liu Tengfei
- 01 Jan 2015
TL;DR: In this article, the authors propose a novel approach to solve the problem of homonymity in homonym-pairing.xiii Chapter 3.3.1.2.
2
•Dissertation
Latent tree models : an application and an extension
Kin Man Poon
- 01 Jan 2012
TL;DR: In this article, the authors propose a novel approach to solve the problem of homonymity in homonym-based homonym identification.xii Chapter 2.xiii Chapter 3
2
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TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
27.2K
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Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten,Eibe Frank,Mark Hall +2 more
- 25 Oct 1999
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
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