Results 1 
9 of
9
Infinite hidden relational models
 In Proceedings of the 22nd International Conference on Uncertainity in Artificial Intelligence (UAI
, 2006
"... Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinitedimensional latent variable as part of a Dirichlet process (DP) mixture model. We d ..."
Abstract

Cited by 64 (20 self)
 Add to MetaCart
Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinitedimensional latent variable as part of a Dirichlet process (DP) mixture model. We discuss inference in the model, which is based on a DP Gibbs sampler, i.e., the Chinese restaurant process. We extend the Chinese restaurant process to be applicable to relational modeling. We discuss how information is propagated in the network of latent variables, reducing the necessity for extensive structural learning. In the context of a recommendation engine our approach realizes a principled solution for recommendations based on features of items, features of users and relational information. Our approach is evaluated in three applications: a recommendation system based on the MovieLens data set, the prediction of gene function using relational information and a medical recommendation system.
Dirichletenhanced spam filtering based on biased samples
 Advances in Neural Information Processing Systems 19
, 2007
"... We study a setting that is motivated by the problem of filtering spam messages for many users. Each user receives messages according to an individual, unknown distribution, reflected only in the unlabeled inbox. The spam filter for a user is required to perform well with respect to this distribution ..."
Abstract

Cited by 45 (8 self)
 Add to MetaCart
(Show Context)
We study a setting that is motivated by the problem of filtering spam messages for many users. Each user receives messages according to an individual, unknown distribution, reflected only in the unlabeled inbox. The spam filter for a user is required to perform well with respect to this distribution. Labeled messages from publicly available sources can be utilized, but they are governed by a distinct distribution, not adequately representing most inboxes. We devise a method that minimizes a loss function with respect to a user’s personal distribution based on the available biased sample. A nonparametric hierarchical Bayesian model furthermore generalizes across users by learning a common prior which is imposed on new email accounts. Empirically, we observe that biascorrected learning outperforms naive reliance on the assumption of independent and identically distributed data; Dirichletenhanced generalization across users outperforms a single (“one size fits all”) filter as well as independent filters for all users. 1
Learning Infinite Hidden Relational Models
"... Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinitestate latent variable as part of a Dirichlet process (DP) mixture model. It can be ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
(Show Context)
Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinitestate latent variable as part of a Dirichlet process (DP) mixture model. It can be viewed as a relational generalization of hidden Markov random field. The information propagates in the internconnected network via latent variables, reducing the necessary for extensive structure learning. For inference, we explore a Gibbs sampling method based on the Chinese restaurant process. The performance of our model is demonstrated in three applications: the movie recommendation, the function prediction of genes and a medical recommendation system.
2007) Incorporating Prior Domain Knowledge into Inductive Machine Learning Its implementation in contemporary capital markets
"... The paper reviews the recent developments of incorporating prior domain knowledge into inductive machine learning, and proposes a guideline that incorporates prior domain knowledge in three key issues of inductive machine learning algorithms: consistency, generalization and convergence. With respect ..."
Abstract

Cited by 6 (0 self)
 Add to MetaCart
(Show Context)
The paper reviews the recent developments of incorporating prior domain knowledge into inductive machine learning, and proposes a guideline that incorporates prior domain knowledge in three key issues of inductive machine learning algorithms: consistency, generalization and convergence. With respect to each issue, this paper gives some approaches to improve the performance of the inductive machine learning algorithms and discusses the risks of incorporating domain knowledge. As a case study, a hierarchical modelling method, VQSVM, is proposed and tested over some imbalanced data sets with various imbalance ratios and various numbers of subclasses. 1.
Learning multiple classifiers with Dirichlet process mixture priors
 In Workshop on Open Problems and Challenges for Nonparametric Bayesian Methods in Machine Learning at Neural Information Processing Systems
, 2005
"... Introduction A real world classification task can often be viewed as consisting of multiple subtasks. In remote sensing, for example, one may have multiple sets of radar images, each collected at a particular geographical location, with the aim of designing classifiers for detecting objects of inte ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Introduction A real world classification task can often be viewed as consisting of multiple subtasks. In remote sensing, for example, one may have multiple sets of radar images, each collected at a particular geographical location, with the aim of designing classifiers for detecting objects of interest in images at all locations. In this situation, one can either learn a single classifier from simple pooling of images from different locations; or learn multi
Dirichlet Enhanced Latent Semantic Analysis
 In Conference in Artificial Intelligence and Statistics
, 2005
"... This paper describes nonparametric Bayesian treatments for analyzing records containing occurrences of items. The introduced model retains the strength of previous approaches that explore the latent factors of each record (e.g. topics of documents), and further uncovers the clustering structur ..."
Abstract
 Add to MetaCart
This paper describes nonparametric Bayesian treatments for analyzing records containing occurrences of items. The introduced model retains the strength of previous approaches that explore the latent factors of each record (e.g. topics of documents), and further uncovers the clustering structure of records, which reflects the statistical dependencies of the latent factors. The nonparametric model induced by a Dirichlet process (DP) flexibly adapts model complexity to reveal the clustering structure of the data. To avoid the problems of dealing with infinite dimensions, we further replace the DP prior by a simpler alternative, namely Dirichletmultinomial allocation (DMA), which maintains the main modelling properties of the DP. Instead of relying on Markov chain Monte Carlo (MCMC) for inference, this paper applies e#cient variational inference based on DMA. The proposed approach yields encouraging empirical results on both a toy problem and text data.
A Uniform Convergence Bound for the Area Under the ROC Curve 1
, 2005
"... The web site for the AISTATS 2005 workshop may be found at ..."
(Show Context)
Proc. 22nd Conf. on Uncertainty in Artificial Intelligence (UAI'06) Cambridge, MA, 2006.
"... Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinitedimensional latent variable as part of a Dirichlet process (DP) mixture model. We d ..."
Abstract
 Add to MetaCart
Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinitedimensional latent variable as part of a Dirichlet process (DP) mixture model. We discuss inference in the model, which is based on a DP Gibbs sampler, i.e., the Chinese restaurant process. We extended the Chinese restaurant process to be applicable to relational modeling. We discuss how information is propagated in the network of latent variables, reducing the necessity for extensive structural learning. In the context of a recommendation engine our approach realizes a principled solution for recommendations based on features of items, features of users and relational information. Our approach is evaluated in three applications: a recommendation system based on the MovieLens data set, the prediction of gene function using relational information and a medical recommendation system.
unknown title
"... Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinitedimensional latent variable as part of a Dirichlet process (DP) mixture model. We d ..."
Abstract
 Add to MetaCart
Relational learning analyzes the probabilistic constraints between the attributes of entities and relationships. We extend the expressiveness of relational models by introducing for each entity (or object) an infinitedimensional latent variable as part of a Dirichlet process (DP) mixture model. We discuss inference in the model, which is based on a DP Gibbs sampler, i.e., the Chinese restaurant process. We extend the Chinese restaurant process to be applicable to relational modeling. We discuss how information is propagated in the network of latent variables, reducing the necessity for extensive structural learning. In the context of a recommendation engine our approach realizes a principled solution for recommendations based on features of items, features of users and relational information. Our approach is evaluated in three applications: a recommendation system based on the MovieLens data set, the prediction of gene function using relational information and a medical recommendation system.