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2012 ISAC Journal Maximum Likelihood
2012 ISAC Journal Maximum Likelihood

... In principle log (L (1,2,3…………..k | data)) = loge (L) defines a “surface" in K-dimensional space, ideas of curvature still apply (as mathematical constructs)[23] [25]. However, plotting is hard for more than 2 parameters. Sampling variances and covariance’s of the MLEs are computed from the log- ...
Music Classification Using Significant Repeating Patterns
Music Classification Using Significant Repeating Patterns

as a PDF
as a PDF

... the predecessor BDD after calculating the relational product. Thus, the algorithm is complete and optimal. However, it is not blindingly fast, so various efforts were made to speed it up, mostly well-known standard techniques in symbolic search such as forward set simplification. A bigger gain in ef ...
Music Classification Using Significant Repeating Patterns
Music Classification Using Significant Repeating Patterns

... representations of music data. A similarity measure considering human perception of music is then designed to measure the similarity degree between two music objects. Finally, we consider a broader coverage of music with seven classes to do performance evaluation (in contrast, [4] only considers the ...
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ANN Models Optimized using Swarm Intelligence Algorithms

... measurement. Measurement can be used to better understand the attributes of the models that are created and used to assess the quality of engineered products or system built. Measure of internal product attributes provides a real-time indication of the efficacy of the requirements, design, code and ...
self-organising map
self-organising map

... •A continuous input space of activation patterns that are generated in accordance with with a certain probability distribution; •A topology of the network in the form of lattice neurons, which defines a discrete output space; •A time-varying neighbourhood that is defined around a winning neuron i(x) ...
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Temporal Pattern Classification using Spiking Neural Networks

... length. Another problem is that the signal should be buffered before it can be processed by the recognition system. In case of processing of audio-signals this only results in a delay of the output. When this method is used for video-recognition, the extra problem of memory-storage becomes significa ...
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...  knowledge representation : It is another great challenge how to express knowledge which can be presented in mathematical or some logical format. Ultimate goal to get a work done by a computer will be to translate the informal sentences into formal ones which could be well interpreted by a computer ...
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Ancient Tamil Script Recognition from Stone Inscriptions Using Slant

Measuring Time Series` Similarity through Large Singular Features
Measuring Time Series` Similarity through Large Singular Features

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Parallels between sensory and motor information

... state of the world given our previous estimate and the current sensory measurement. One way to do this is to use a predictor-corrector method: combine the previous estimate with a model of one-step dynamics to obtain a prediction of the current state, and then correct the prediction so as to make it ...
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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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