Patterns in pattern recognition: 1968-1974

Kanal, Laveen N.

doi:10.1109/tit.1974.1055306

Cited by 252 publications

(22 citation statements)

References 166 publications

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“…(Class I) as the unkniown class is fixed to be 0.05, according to ha' aa a t a aas o a l cr-i tea-ion -i t h e regi on-i associ ated with the known al a ss L N i s [-1.645 , 1.645] at-id the probability of muui sclassif yi nq tht ' unikniowni class iS the known-i class is a e r f c ( l . 6 4 5 -x ) f e rfc( I.645+ x ) (95) i-t }aa 'i'e x is the noise free signal of the unknown class and the function erfc (x ) is defined in Equation (2 1).…”

Section: Anothem -Comm Inmoni S I T U a T I O N Similar To This Occurmentioning

confidence: 99%

Identification of Catalogued and Uncatalogued Classes.

Lin

1978

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Section: Anothem -Comm Inmoni S I T U a T I O N Similar To This Occurmentioning

confidence: 99%

Identification of Catalogued and Uncatalogued Classes.

Lin

1978

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“…It is clear that no single model exists for all pattern recognition problems and no single technique is applicable to all problems. Rather, what we have is a bag of tools and a bag of problems [1]. Despite the numerous work in the field, that did not allow to highlight the indisputable superiority of one method of classification to another or a feature selection on another.…”

Section: Introductionmentioning

confidence: 99%

“…As the first step in the identification process, in September 2006 Wu et al invited ACM KDD 1 Innovation Award and IEEE ICDM 2 Research Contributions Award winners to each nominate up to 10 best-known algorithms in data mining.…”

Section: Introductionmentioning

confidence: 99%

“…In the third step of the identification process, a wider involvement 1 In classification task, the choice of classifier in this long list of methods is primordial for a better recognition and this especially on the medical field. Modern medicine needs computer assistance for detection, diagnosis and classification of certain diseases in a very short time hence the need for a classification system.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classi cation Task

Settouti¹,

Bechar²,

Chikh³

2016

IJIMAI

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-This work is builds on the study of the 10 top data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) community in December 2006. We address the same study, but with the application of statistical tests to establish, a more appropriate and justified ranking classifier for classification tasks. Current studies and practices on theoretical and empirical comparison of several methods, approaches, advocated tests that are more appropriate. Thereby, recent studies recommend a set of simple and robust non-parametric tests for statistical comparisons classifiers. In this paper, we propose to perform non-parametric statistical tests by the Friedman test with post-hoc tests corresponding to the comparison of several classifiers on multiple data sets. The tests provide a better judge for the relevance of these algorithms.

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“…Particularly, the MLP model applied to pattern recognition [12][13][14][15][16] process has lead to important advances and shown potential as a classification system. It has been successfully applied to pattern classification tasks.…”

Section: Introductionmentioning

confidence: 99%

An Experimentation with Improved Target Vectors for MLP in Classifying Degraded Patterns

Nomura¹,

Manzan²,

Yamanaka³

2010

L&NLM

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-In this paper, we adopt unconventional target vectors to improve the performance of pattern classification systems using neural network techniques based on MLP. Instead of conventional target vectors, the new target vectors are bipolar, orthogonal, and highly dimensional. Since they are orthogonal with bipolar representation, we can take advantage of increasing on the Euclidean distance for these vectors when their number (n in a Euclidean space R n ) of components increases. We define non-orthogonal bipolar vectors considered as conventional target vectors for comparison purposes. Those non-orthogonal bipolar vectors provide a fair reference to ensure the effectiveness of the adopted unconventional target vectors and in justifying the credibility and validity of experimental results. The conventional and unconventional target vectors are used in the experiments for training MLP models by backpropagation algorithm to classify patterns extracted from actual degraded images. Comparison of experimental results lead to conclusions that classification performances of MLP models considerably improved with the adopted unconventional target vectors in classifying degraded patterns.

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Patterns in pattern recognition: 1968-1974

Cited by 252 publications

References 166 publications

Identification of Catalogued and Uncatalogued Classes.

Identification of Catalogued and Uncatalogued Classes.

Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classi cation Task

An Experimentation with Improved Target Vectors for MLP in Classifying Degraded Patterns

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