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Full Paper (PDF 376832 bytes). - Vanderbilt University School of
Full Paper (PDF 376832 bytes). - Vanderbilt University School of



ppt
ppt

Towards Real-time Probabilistic Risk Assessment by
Towards Real-time Probabilistic Risk Assessment by

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Machine Condition Monitoring Using Artificial Intelligence: The

... Machine condition monitoring is gaining importance in industry due to the need to increase machine reliability and decrease the possible loss of production due to machine breakdown. Often the data available to build a condition monitoring system does not fully represent the system. It is also often ...
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Marking Period 1 Math Newsletter Part 2

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More Mathematics into Medicine!

... of technique for routine application. At present, each image frame is treated separately, a regularization with respect to time, as would be necessary, e.g., in current intensity reconstruction from MEG/EEG data, is not yet implemented. Electron paramagnetic resonance imaging. In this technique a d ...
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Invited Paper Neural networks in engineering D.T. Pham Intelligent

... each neuron in the input layer and an attempt is made to train each neuron to produce that output. When the sum of the mean square errors S% over all the desired outputs in the training data set for a given neuron reaches the minimum for that neuron, the weights of the neuron are frozen and its trai ...
Tutorial "Computational intelligence for data mining"
Tutorial "Computational intelligence for data mining"

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Estimating the entropy of a signal with applications

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M.Sc. Aleksandar Bučkovski, B.Sc. Aleksandar

... form in the image. Main idea in line detection process is that every dot of the form have influence on a global solution. For example we can take one problem of finding dots direction shown. Pic 5а). Possible solution are shown at Pic 5b) and c). ...
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Structured Regularizer for Neural Higher

... Higher-order LC-CRFs (HO-LC-CRFs) allow for arbitrary input-independent (such factors depend on the output labels only) [31] and input-dependent (such factors depend on both the input and output variables) higher-order factors [21, 14]. That means both types of factors can include more than two outp ...
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A bio-inspired learning signal for the cumulative learning - laral

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Moments of Satisfaction: Statistical Properties of a Large Random K-CNF formula

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MATH 304 Linear Algebra Lecture 16b: Euclidean structure in R

... Let v be a vector and r ∈ R. By definition, r v is a vector whose magnitude is |r | times the magnitude of v. The direction of r v coincides with that of v if r > 0. If r < 0 then the directions of r v and v are ...
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Machine Learning - Department of Computer Science

Modeling Human-Level Intelligence by Integrated - CEUR
Modeling Human-Level Intelligence by Integrated - CEUR

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Machine Learning

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Models Of Cognition

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INTCare: A Knowledge Discovery based Intelligent Decision

... - regression - maps a data item into a real-value variable (e.g. estimation of the patient’s heart rate); and - clustering - searches for natural groupings of objects based on similarity measures (e.g. segmenting patients into clusters according to similar profiles). The above goals may involve the ...
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Computational Intelligence in Data Mining

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FEATURE-TAK - Framework for Extraction, Analysis, and

... Several methods from these frameworks are used by our framework, too. But we also combine them with techniques from association rule mining, case-based reasoning, and techniques developed in-house to have a direct use for knowledge modeling in CBR systems. There is extensive research pertaining to a ...
powerpoint - University of York
powerpoint - University of York

... For example, C-rater, a program that emphasizes semantic content in essays, yet has no representation of semantic content other than as desirable features for the essay. ...
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Restoration of Hyperspectral Push-Broom Scanner Data

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Decision DAGS – A new approach

< 1 ... 76 77 78 79 80 81 82 83 84 ... 193 >

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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