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Multilayer Networks
Multilayer Networks

View PDF - CiteSeerX
View PDF - CiteSeerX

... complex numerical model is used. In this case domain knowledge is embodied in a model that generates simulated events according to the currently held physics model as well as a simulation of the response of the detector to the newly created particles. This simulates what the trigger system sees. The ...
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... sensory features [20], [23] to motor area for generating behaviors. It has been reported that feed-backward connections can serve as class supervision [7], attention [2], [3], and storage of time information [33]. Foreseeably, there are many other functions to which we can attribute feed-backward co ...
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... Intuition is a very much a human skill. We all have hunches and “gut feelings” about what to do, what’s right, and what’s wrong. But intuition without hard data to back it up seldom leads to an ideal choice. Optimization happens when data drives a decision and supplements it—at key moments—with huma ...
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Which Value x Best Represents a Sample x1, ..., xn: Utility

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Research Journal of Applied Sciences, Engineering and Technology 6(3): 450-456,... ISSN: 2040-7459; e-ISSN: 2040-7467

... in Eq. (1) has been simulated using the given scheme to see the response of the system and applicability of the proposed scheme. The desired response [32] of the model is represented by the relation y(t) = (1+ t2 /3)-0.5 and is calculated in an entire finite domain between 0 and 1. This will work as ...
Linear Algebra in R
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... Quantitative precipitation forecast is extremely important for lives and safety of people living in Japan especially because the Japanese islands are located at low latitude and heavy rainfalls sometimes bring serious damage to the lives and the properties of people living in the Japanese islands. T ...
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Pattern recognition

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled ""training"" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition has its origins in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition. In pattern recognition, there may be a higher interest to formalize, explain and visualize the pattern, while machine learning traditionally focuses on maximizing the recognition rates. Yet, all of these domains have evolved substantially from their roots in artificial intelligence, engineering and statistics, and they've become increasingly similar by integrating developments and ideas from each other.In machine learning, pattern recognition is the assignment of a label to a given input value. In statistics, discriminant analysis was introduced for this same purpose in 1936. An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is ""spam"" or ""non-spam""). However, pattern recognition is a more general problem that encompasses other types of output as well. Other examples are regression, which assigns a real-valued output to each input; sequence labeling, which assigns a class to each member of a sequence of values (for example, part of speech tagging, which assigns a part of speech to each word in an input sentence); and parsing, which assigns a parse tree to an input sentence, describing the syntactic structure of the sentence.Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform ""most likely"" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns. A common example of a pattern-matching algorithm is regular expression matching, which looks for patterns of a given sort in textual data and is included in the search capabilities of many text editors and word processors. In contrast to pattern recognition, pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms (especially with fairly general, carefully tailored patterns) can sometimes succeed in providing similar-quality output of the sort provided by pattern-recognition algorithms.
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