Publications related to Sliding Window Abstraction for Infinite Markov Chains

Internet-scale storage systems under churn - A study of the steady state using Markov models

Content storage in a distributed collaborative environment uses redundancy for better resilience and thus provides good availability and durability. In a peer-to-peer environment, where peers continuously leave and rejoin the network, various lazy strategi ...

IEEE Computer Society2006

Asset trading volume in infinite-horizon economies with dynamically complete markets and heterogeneous agents: Comment

In a recent paper, Judd et al. [2003. Journal of Finance 58, 2203-2217] study asset trading in a version of the standard Lucas infinite horizon economy with heterogeneous agents. They report the surprising finding that (for generic economies in their class ...

2006

Gradient estimates of return distributions

Samy Bengio, Christos Dimitrakakis

We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distributions over actions in value-based reinforcement learning. While this approach is simi ...

2005

Boosting word error rates

Samy Bengio, Christos Dimitrakakis

We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...

2005

Boosting word error rates

Samy Bengio, Christos Dimitrakakis

We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...

2005

Learning influence among interacting Markov chains

Daniel Gatica-Perez, Samy Bengio, Dong Zhang

We present a model that learns the influence of interacting Markov chains within a team. The proposed model is a dynamic Bayesian network (DBN) with a two-level structure: individual-level and group-level. Individual level models actions of each player, an ...

IDIAP2005

Learning influence among interacting Markov chains

Daniel Gatica-Perez, Samy Bengio, Dong Zhang

We present a model that learns the influence of interacting Markov chains within a team. The proposed model is a dynamic Bayesian network (DBN) with a two-level structure: individual-level and group-level. Individual level models actions of each player, an ...

2005

Boosting word error rates

Samy Bengio, Christos Dimitrakakis

We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...

IDIAP2004

Text Segmentation and Recognition in Complex Background Based on Markov Random Field

Hervé Bourlard, Jean-Marc Odobez, Datong Chen

In this paper we propose a method to segment and recognize text embedded in video and images. We modelize the gray level distribution in the text images as mixture of gaussians, and then assign each pixel to one of the gaussian layer. The assignment is bas ...

2002

Text Segmentation and Recognition in Complex Background Based on Markov Random Field

Hervé Bourlard, Jean-Marc Odobez, Datong Chen

In this paper we propose a method to segment and recognize text embedded in video and images. We modelize the gray level distribution in the text images as mixture of gaussians, and then assign each pixel to one of the gaussian layer. The assignment is bas ...

IDIAP2002

Sliding Window Abstraction for Infinite Markov Chains

Graph Chatbot

Chat with Graph Search

Internet-scale storage systems under churn - A study of the steady state using Markov models

Asset trading volume in infinite-horizon economies with dynamically complete markets and heterogeneous agents: Comment

Gradient estimates of return distributions

Boosting word error rates

Boosting word error rates

Learning influence among interacting Markov chains

Learning influence among interacting Markov chains

Boosting word error rates

Text Segmentation and Recognition in Complex Background Based on Markov Random Field

Text Segmentation and Recognition in Complex Background Based on Markov Random Field

Gradient estimates of return distributions

Asset trading volume in infinite-horizon economies with dynamically complete markets and heterogeneous agents: Comment

Learning influence among interacting Markov chains

Learning influence among interacting Markov chains

Text Segmentation and Recognition in Complex Background Based on Markov Random Field

Text Segmentation and Recognition in Complex Background Based on Markov Random Field

Internet-scale storage systems under churn - A study of the steady state using Markov models

Boosting word error rates

Boosting word error rates

Boosting word error rates