Results 1  10
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20
Hierarchical Latent Class Models for Cluster Analysis
 Journal of Machine Learning Research
, 2002
"... Latent class models are used for cluster analysis of categorical data. Underlying such a model is the assumption that the observed variables are mutually independent given the class variable. A serious problem with the use of latent class models, known as local dependence, is that this assumption is ..."
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Cited by 46 (12 self)
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Latent class models are used for cluster analysis of categorical data. Underlying such a model is the assumption that the observed variables are mutually independent given the class variable. A serious problem with the use of latent class models, known as local dependence, is that this assumption is often untrue. In this paper we propose hierarchical latent class models as a framework where the local dependence problem can be addressed in a principled manner. We develop a searchbased algorithm for learning hierarchical latent class models from data. The algorithm is evaluated using both synthetic and realworld data.
Learning Module Networks
, 2003
"... Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and statistical problems in domains that involve a large number of variables. In this paper, we ..."
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Cited by 44 (4 self)
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Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computational and statistical problems in domains that involve a large number of variables. In this paper, we
Learning hidden variable networks: The information bottleneck approach
 Journal of Machine Learning Research
, 2005
"... A central challenge in learning probabilistic graphical models is dealing with domains that involve hidden variables. The common approach for learning model parameters in such domains is the expectation maximization (EM) algorithm. This algorithm, however, can easily get trapped in suboptimal local ..."
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Cited by 23 (0 self)
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A central challenge in learning probabilistic graphical models is dealing with domains that involve hidden variables. The common approach for learning model parameters in such domains is the expectation maximization (EM) algorithm. This algorithm, however, can easily get trapped in suboptimal local maxima. Learning the model structure is even more challenging. The structural EM algorithm can adapt the structure in the presence of hidden variables, but usually performs poorly without prior knowledge about the cardinality and location of the hidden variables. In this work, we present a general approach for learning Bayesian networks with hidden variables that overcomes these problems. The approach builds on the information bottleneck framework of Tishby et al. (1999). We start by proving formal correspondence between the information bottleneck objective and the standard parametric EM functional. We then use this correspondence to construct a learning algorithm that combines an informationtheoretic smoothing term with a continuation procedure. Intuitively, the algorithm bypasses local maxima and achieves superior solutions by following a continuous path from a solution of, an easy and smooth, target function, to a solution of the desired likelihood function. As we show, our algorithmic framework allows learning of the parameters as well as the structure of a network. In addition, it also allows us to introduce new hidden variables during model selection and learn their cardinality. We demonstrate the performance of our procedure on several challenging reallife data sets.
Latent Tree Models and Approximate Inference in Bayesian Networks
 TO APPEAR, AAAI08
, 2008
"... We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and when online, make inference with the LTM instead of the original BN. Because LTMs are treestructured, inference takes line ..."
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Cited by 12 (4 self)
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We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and when online, make inference with the LTM instead of the original BN. Because LTMs are treestructured, inference takes linear time. In the meantime, they can represent complex relationship among leaf nodes and hence the approximation accuracy is often good. Empirical evidence shows that our method can achieve good approximation accuracy at low online computational cost.
Fast state discovery for HMM model selection and learning
 In Proc. Int’l Conference on Artificial Intelligence and Statistics
, 2007
"... Choosing the number of hidden states and their topology (model selection) and estimating model parameters (learning) are important problems for Hidden Markov Models. This paper presents a new statesplitting algorithm that addresses both these problems. The algorithm models more information about th ..."
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Cited by 11 (3 self)
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Choosing the number of hidden states and their topology (model selection) and estimating model parameters (learning) are important problems for Hidden Markov Models. This paper presents a new statesplitting algorithm that addresses both these problems. The algorithm models more information about the dynamic context of a state during a split, enabling it to discover underlying states more effectively. Compared to previous topdown methods, the algorithm also touches a smaller fraction of the data per split, leading to faster model search and selection. Because of its efficiency and ability to avoid local minima, the statesplitting approach is a good way to learn HMMs even if the desired number of states is known beforehand. We compare our approach to previous work on synthetic data as well as several realworld data sets from the literature, revealing significant improvements in efficiency and testset likelihoods. We also compare to previous algorithms on a signlanguage recognition task, with positive results. 1
Classification using Hierarchical Naïve Bayes models
 Machine Learning 2006
, 2002
"... Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well performing set of classifiers is the Nave Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an in ..."
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Cited by 11 (1 self)
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Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well performing set of classifiers is the Nave Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to "information doublecounting" and interaction omission.
Latent Variable Discovery in Classification Models
, 2004
"... The naive Bayes model makes the often unrealistic assumption that feature variables are mutually independent given the class variable. We interpret the violation of this assumption as an indication of the presence of latent variables and show how latent variables can be detected. Latent variable dis ..."
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Cited by 10 (2 self)
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The naive Bayes model makes the often unrealistic assumption that feature variables are mutually independent given the class variable. We interpret the violation of this assumption as an indication of the presence of latent variables and show how latent variables can be detected. Latent variable discovery is interesting, especially for medical applications, because it can lead to better understanding of application domains. It can also improve classification accuracy and boost user confidence in classification models.
Tractable probabilistic models for intention recognition based on expert knowledge
 In Intl. Conf. Intel. Rob. Sys
, 2007
"... Abstract — Intention recognition is an important topic in humanrobot cooperation that can be tackled using probabilistic modelbased methods. A popular instance of such methods are Bayesian networks where the dependencies between random variables are modeled by means of a directed graph. Bayesian n ..."
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Cited by 9 (0 self)
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Abstract — Intention recognition is an important topic in humanrobot cooperation that can be tackled using probabilistic modelbased methods. A popular instance of such methods are Bayesian networks where the dependencies between random variables are modeled by means of a directed graph. Bayesian networks are very efficient for treating networks with conditionally independent parts. Unfortunately, such independence sometimes has to be constructed by introducing so called hidden variables with an intractably large state space. An example are human actions which depend on human intentions and on other human actions. Our goal in this paper is to find models for intentionaction mapping with a reduced state space in order to allow for tractable online evaluation. We present a systematic derivation of the reduced model and experimental results of recognizing the intention of a real human in a virtual environment. I.
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains
, 2003
"... When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this inf ..."
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Cited by 9 (0 self)
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When constructing a Bayesian network, it can be advantageous to employ structural learning algorithms to combine knowledge captured in databases with prior information provided by domain experts. Unfortunately, conventional learning algorithms do not easily incorporate prior information, if this information is too vague to be encoded as properties that are local to families of variables. For instance, conventional algorithms do not exploit prior information about repetitive structures, which are often found in object oriented domains such as computer networks, large pedigrees and genetic analysis.
Generalized measurement models
, 2004
"... Given a set of random variables, it is often the case that their associations can be explained by hidden common causes. We present a set of welldefined assumptions and a provably correct algorithm that allow us to identify some of such hidden common causes. The assumptions are fairly general and so ..."
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Cited by 7 (4 self)
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Given a set of random variables, it is often the case that their associations can be explained by hidden common causes. We present a set of welldefined assumptions and a provably correct algorithm that allow us to identify some of such hidden common causes. The assumptions are fairly general and sometimes weaker than those used in practice by, for instance, econometricians, psychometricians, social scientists and in many other fields where latent variable models are important and tools such as factor analysis are applicable. The goal is automated knowledge discovery: identifying latent variables that can be used across diferent applications and causal models and throw new insights over a data generating process. Our approach is evaluated throught simulations and three realworld cases.