By Ron Sun (auth.), Ron Sun, C. Lee Giles (eds.)
ISBN-10: 3540415971
ISBN-13: 9783540415978
Sequential habit is vital to intelligence often and a primary a part of human actions, starting from reasoning to language, and from daily talents to advanced challenge fixing. series studying is a crucial section of studying in lots of projects and alertness fields: making plans, reasoning, robotics ordinary language processing, speech acceptance, adaptive regulate, time sequence prediction, monetary engineering, DNA sequencing, etc. This publication provides coherently built-in chapters by means of major experts and assesses the cutting-edge in series studying through introducing crucial types and algorithms and via reading quite a few purposes. The publication deals topical sections on series clustering and studying with Markov versions, series prediction and popularity with neural networks, series discovery with symbolic equipment, sequential selection making, biologically encouraged series studying models.
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Additional resources for Sequence Learning: Paradigms, Algorithms, and Applications
Example text
Suppose one has a multivariate time series of k variable each of which takes, say, v values. A first solution is trivial and consists of recoding these series into a single one, as it was generated by a variable taking v k values, one for each combination of values of the original variables. The difficulty here is computational: the transition probability table for the encoding variable will have (v k )2 probabilities to estimate. A more sophisticated approach relies on some assumptions of conditional independence among the variable dynamics so that each cluster becomes a set of mcs with independent transition probability tables (Ramoni, Sebastiani, & Cohen, 2000).
X (α = 5) discriminate the sensor dynamics when an object is far from the robot, or near the robot or a mixture of the two. y (α = 1) distinguish among an object moving 28 P. Sebastiani, M. Ramoni, and P. Cohen from the left of the robot’s visual field to the center, from the left to the right, from the right to the left, or in front of the robot. The interpretation of the dynamic processes, represented by the clusters that the bcd algorithms found for each sensor, is our own and not the robot’s one.
5 Future and Related Work Although the description of bcd in this paper assumes a batch of univariate time series for reasons of simplicity, we have extended it to the multivariate case. Suppose one has a multivariate time series of k variable each of which takes, say, v values. A first solution is trivial and consists of recoding these series into a single one, as it was generated by a variable taking v k values, one for each combination of values of the original variables. The difficulty here is computational: the transition probability table for the encoding variable will have (v k )2 probabilities to estimate.
Sequence Learning: Paradigms, Algorithms, and Applications by Ron Sun (auth.), Ron Sun, C. Lee Giles (eds.)
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