Building Knowledge Bases through Multistrategy Learning and
... extremely difficult for one may be easy for the other. For instance, automated learning systems have traditionally had difficulty assigning credit or blame to individual decisions that lead to overall results, but this process is generally easy for a human expert. Also, the “new terms” problem in th ...
... extremely difficult for one may be easy for the other. For instance, automated learning systems have traditionally had difficulty assigning credit or blame to individual decisions that lead to overall results, but this process is generally easy for a human expert. Also, the “new terms” problem in th ...
Aalborg Universitet Learning dynamic Bayesian networks with mixed variables Bøttcher, Susanne Gammelgaard
... In a Bayesian network, the set of random variables X is fixed. To model a multivariate time series we need a framework, where we allow the set of random variables to vary with time. For this we use dynamic Bayesian networks, defined as below. This definition is consistent with the exposition in Murp ...
... In a Bayesian network, the set of random variables X is fixed. To model a multivariate time series we need a framework, where we allow the set of random variables to vary with time. For this we use dynamic Bayesian networks, defined as below. This definition is consistent with the exposition in Murp ...
Inferences on the means of lognormal distributions using
... sample inference is called for. In fact, in the context of analyzing occupational exposure data using the lognormal distribution, Lyles et al. (1997, p. 69) mention that “personal exposure monitoring is relatively time consuming and costly, so typical samples will seldom be large in a statistical se ...
... sample inference is called for. In fact, in the context of analyzing occupational exposure data using the lognormal distribution, Lyles et al. (1997, p. 69) mention that “personal exposure monitoring is relatively time consuming and costly, so typical samples will seldom be large in a statistical se ...
Autonomous Units
... End Goals, particular states the agent tries to achieve Adopted from http://www.rt.el.utwente.nl/agent/ Selective reinforcement or reward that the agent attempts to maximize Internal needs or motivations that the agent has to keep within certain viability zones. Modeling Adaptive Autonomous Ag ...
... End Goals, particular states the agent tries to achieve Adopted from http://www.rt.el.utwente.nl/agent/ Selective reinforcement or reward that the agent attempts to maximize Internal needs or motivations that the agent has to keep within certain viability zones. Modeling Adaptive Autonomous Ag ...
Leveraging the upcoming disruptions from AI and IoT
... However, as companies direct increasing investment into AI over the next few years, the impacts will extend far beyond business performance—simultaneously generating massive change in the number and nature of jobs in many industries. Essentially, we are set to see growing use of AI in every job and ...
... However, as companies direct increasing investment into AI over the next few years, the impacts will extend far beyond business performance—simultaneously generating massive change in the number and nature of jobs in many industries. Essentially, we are set to see growing use of AI in every job and ...
Laminar Selectivity of the Cholinergic Suppression of Synaptic
... output to be formed by self-organization of the synapses in s. l-m. In keeping with physiological evidence showing that synapses in s. l-m have a much weaker influence than synapses in s. rad (Andersen et al., 1966; Doller and Weight, 1982; Yeckel and Berger, 1990; Colbert and Levy, 1992), each syna ...
... output to be formed by self-organization of the synapses in s. l-m. In keeping with physiological evidence showing that synapses in s. l-m have a much weaker influence than synapses in s. rad (Andersen et al., 1966; Doller and Weight, 1982; Yeckel and Berger, 1990; Colbert and Levy, 1992), each syna ...
Discriminative Structure and Parameter Learning for Markov
... from scratch. Can handle problems with thousands of small structured ...
... from scratch. Can handle problems with thousands of small structured ...
Pareto Optimal Solutions Visualization Techniques for Multiobjective
... Notice that the optimal solution from the 2D POS for precision does not provide any residual precision or error detectability capabilities. Precision is always a very important factor in the design of sensor networks. The candidate solution from the precision 2D POS has a good (low) value for the pr ...
... Notice that the optimal solution from the 2D POS for precision does not provide any residual precision or error detectability capabilities. Precision is always a very important factor in the design of sensor networks. The candidate solution from the precision 2D POS has a good (low) value for the pr ...
Using extended feature objects for partial similarity
... key to cost reduction. Although originating from a rather specific application, the problem of finding all partially similar polygons from a database of 2D polygons is a general problem which arises in many applications such as CAD, pattern recognition, protein docking, computer tomography and other ...
... key to cost reduction. Although originating from a rather specific application, the problem of finding all partially similar polygons from a database of 2D polygons is a general problem which arises in many applications such as CAD, pattern recognition, protein docking, computer tomography and other ...
An Introduction to Variational Methods for Graphical Models
... The junction tree algorithm compiles directed graphical models into undirected graphical models; subsequent inferential calculation is carried out in the undirected formalism. The step that converts the directed graph into an undirected graph is called “moralization.” (If the initial graph is alread ...
... The junction tree algorithm compiles directed graphical models into undirected graphical models; subsequent inferential calculation is carried out in the undirected formalism. The step that converts the directed graph into an undirected graph is called “moralization.” (If the initial graph is alread ...
