[PDF] Top 20 Distributed intelligent illumination control in the context of probabilistic graphical models
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Distributed intelligent illumination control in the context of probabilistic graphical models
... Different illumination control approaches exist, depending on the system architecture and optimization ...the illumination control problem as a tradeoff between energy efficiency and user ...a ... See full document
6
Structure learning of context-specific graphical models
... generalize probabilistic graphical models such as Bayesian networks and Markov ...of context-specific graphical models in which CSI is included as part of the model ... See full document
35
Probabilistic User Behavior Models
... mixture models and mixture of Markov models for inferring individualized behavior models of Web users, where a behavior model is a probabilistic model de- scribing which actions the user will ... See full document
8
Marginal pseudolikehood in labelled graphical models
... Probabilistic graphical models are used to represent high dimensional distributions in a simple, compact way in a wide variety of applications spanning from physics to biology and sociology (see ... See full document
33
Combining Textual and Graph-based Features for Named Entity Disambiguation Using Undirected Probabilistic Graphical Models
... The features used in entity disambiguation models vary widely. Many ap- proaches rely on features that measure textual coherence. This is typically im- plemented by a measure of similarity between the ... See full document
15
New Probabilistic Graphical Models and Meta-Learning Approaches for Hierarchical Classification, with Applications in Bioinformatics and Ageing
... data, models that classify objects from the problem domain (instances) into categories (class labels), given some attributes that describe those ...this context, we call the data that the algorithm uses to ... See full document
217
Online Bayesian Learning in Probabilistic Graphical Models using Moment Matching with Applications
... measures acceleration across x, y and z axis. It also has four load cells mounted just above the wheels that measure the weight distribution on each leg of the walker. There is also a wheel encoder that measures the ... See full document
139
Garnata: An Information Retrieval System for Structured Documents based on Probabilistic Graphical Models
... The aim of this paper is to describe the archi- tecture of Garnata, studying the system per- formance but not the model effectiveness. In order to expose our ideas, this paper is divided into the following sections: the ... See full document
8
Human Action Recognition Using Deep Probabilistic Graphical Models
... shape context into a spherical coordinate system, they model a human activity using a hierarchy of 3D skeletal features in motion and learn the dynamics of these features using Linear Dynamical Systems ... See full document
136
Unsupervised document zone identification using probabilistic graphical models
... Previous work on automatic labelling of document zones mostly employ supervised machine learning, using widely known classifiers such as Naive Bayes (Teufel and Moens, 2002), Hidden Markov Model (Li et al., 2010), ... See full document
8
Intrusion detection using probabilistic graphical models
... typically first train a model based on selective features using a certain amount of data, then predict a future data record to be intrusive or normal based on the trained model. However, one common limitation of all ... See full document
89
Learning Latent Tree Graphical Models
... cluster models (LCM) consider multivariate distributions in which there exists only one latent variable and each state of that variable corresponds to a cluster in the data (Lazarsfeld and Henry, ...(HLC) ... See full document
42
PC Algorithm for Nonparanormal Graphical Models
... in graphical mod- elling with directed graphs via a clever scheme of testing conditional ...requires control of the conditioning of principal submatrices of correlation matrices that are inverted to ... See full document
19
Unraveling temporal processes using probabilistic graphical models
... Nowadays, a significant portion of the population has more than one chronic disease at the same time, which is known as the problem of multimorbidity. Better understanding multimorbidity is hindered by the fact that most ... See full document
191
On Semiparametric Exponential Family Graphical Models
... mixed graphical models, we allow the nodewise conditional distributions to be semiparametric generalized linear models with un- specified base measure ... See full document
59
Adaptive Exact Inference in Graphical Models
... There are numerous machine learning and artificial intelligence problems, such as path planning problems in robotics, where new information or observations require changing a previously com- puted solution. As an ... See full document
40
Sparse graphical models for cancer signalling
... an intracellular receptor that activates when bound by the hormone estrogen. Ap- proximately 75% of breast cancers are dependent on ER for proliferation. Activated ER can usually be found in the cell nucleus, where it ... See full document
214
Automatic Headlamp Illumination Control System
... headlamp control system, driver has control of the headlight which can be switched from high beam (bright) to low beam (dim), this is insufficient while driving on curved ...in illumination of the ... See full document
6
Distributed Support for Intelligent Environments
... the Intelligent Environment are how to enable transparent, distributed computing to allow continued operation across changing circumstances and how to exploit the changing environment so that it is aware of ... See full document
19
Exploring Word Order Universals: a Probabilistic Graphical Model Approach
... There are two advantages of using this model to study word order universals. First the graphical structure can reveal much finer structure of lan- guage as a complex system. Most studies on word order ... See full document
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