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inputs and a summing device (Σ). Any such RAM-discriminator can receive a binary pattern of X⋅n bits as input. The RAM input lines are connected to the input pattern by means of a biunivocal pseudo-random mapping. The summing device enables this network of RAMs to exhibit – just like other ANN models
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In order to train the discriminator one has to set all RAM memory locations to 0 and choose a training set formed by binary patterns of Xâ‹…n bits. For each training pattern, a 1 is stored in the memory location of each RAM addressed by this input pattern. Once the training of patterns is completed,
1102:
A system formed by various RAM-discriminators is called WiSARD. Each RAM-discriminator is trained on a particular class of patterns, and classification by the multi-discriminator system is performed in the following way. When a pattern is given as input, each RAM-discriminator gives a response to
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of the highest response (e.g., the difference d between the highest response and the second highest response, divided by the highest response). A schematic representation of a RAM-discriminator and a 10 RAM-discriminator WiSARD is shown in Figure 1.
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The information stored by the RAM during the training phase is used to deal with previous unseen patterns. When one of these is given as input, the RAM memory contents addressed by the input pattern are read and summed by ÎŁ. The number
1099:-bit component of the input pattern appears in the training set (not a single RAM outputs 1). Intermediate values of r express a kind of “similarity measure” of the input pattern with respect to the patterns in the training set.
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Advances in computational intelligence and learning : 17th
European Symposium on Artificial Neural Networks ; ESANN 2009 ; Bruges, Belgium, April 22-23-24, 2009 ; proceedings
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and unset otherwise. Recognition is accomplished by summing the contents of the nodes of each class at the addresses given by the features of the unclassified pattern. That is, pattern
1170:. Verleysen, Michel, Université catholique de Louvain, ESANN (17 2009.04.22-24 Bruges), European Symposium on Artificial Neural Networks (17 2009.04.22-24 Bruges). Evere: d-side. 2009.
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The RAMnets formed the basis of a commercial product known as WiSARD (Wilkie, Stonham and
Aleksander Recognition Device) was the first artificial neural network machine to be patented.
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321:{\displaystyle {\begin{aligned}{\underset {c}{a}}rgmax(\sum _{i=1}^{N}\Theta (\sum _{v\in D_{c}}\delta (\alpha _{i}(u),\alpha _{i}(v))))\end{aligned}}}
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With C classes to distinguish, the system can be implemented as a network of NC nodes, each of which is a random access memory (RAM); hence the term
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A pattern is classified as belonging to the class for which it has the most features in common with at least one training pattern of that class.
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thus obtained, which is called the discriminator response, is equal to the number of RAMs that output 1. r reaches the maximum
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that input. The various responses are evaluated by an algorithm which compares them and computes the relative confidence
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1049:{\displaystyle {\begin{aligned}{\underset {c}{a}}rgmax(\sum _{i=1}^{N}m_{ci\alpha }(u))\end{aligned}}}
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Y. Guan,J.G. Taylor,D. Gorse, .G. Clarkson (1993). "Generalization in probabilistic RAM nets".
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An introductory tutorial to classifiers (introducing the basic terms, with numeric example)
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Applied
Pattern Recognition: A Practical Introduction to Image and Speech Processing in C++
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167:= 0 case of a more general rule whereby the class assigned to unclassified pattern u is
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The
Elements of Statistical Learning: Data mining, Inference, and Prediction
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862:{\displaystyle \Theta (\sum _{v\in D_{c}}\delta (\alpha ,\alpha _{i}(v)))}
127:-tuples. The restriction of a pattern to an n-tuple can be regarded as an
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RAM memory contents will be set to a certain number of 0’s and 1’s.
1341:. Springer Science+Business Media New York: Springer, Boston, MA.
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based on synaptic weights – generalization and noise tolerance.
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is one of the oldest practical neurally inspired classification
26:
135:-tuple, constitutes a `feature' of the pattern. The standard
1383:(2nd ed.). San Francisco: Morgan Kaufmann Publishers.
108:-tuple recognition method" or "weightless neural network".
121:) sets of n distinct bit locations are selected randomly.
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Unsupervised
Learning: Foundations of Neural Computation
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131:-bit number which, together with the identity of the
1379:Hornegger, Joachim; Paulus, Dietrich W. R. (1999).
639:{\displaystyle \sum _{j=0}^{n-1}u_{\eta }i(j)2^{j}}
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1324:A brief introduction to Weightless NeuralSystems
1273:(This book focuses on unsupervised learning in
54:but its sources remain unclear because it lacks
1360:Introduction to Statistical Pattern Recognition
139:-tuple recognizer operates simply as follows:
751:of the i node allocated to class c is set to
8:
1315:: CS1 maint: multiple names: authors list (
334:is the set of training patterns in class c,
1219:Hastie, Trevor; Tibshirani, Robert (2009).
1211:Michal Morciniec and Richard Rohwer(1995) "
104:. The RAMnets is also known as a type of "
1213:The n-tuple Classifier: Too Good to Ignore
1091:if the input belongs to the training set.
1066:A RAM-discriminator consists of a set of
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85:Learn how and when to remove this message
1364:(2nd ed.). Boston: Academic Press.
1223:. New York: Springer. pp. 485–586.
691:is the j bit location of the i n-tuple.
1159:
1308:
1191:
7:
1337:N. M. Allinson, A. R. Kolcz (1997).
1283:IEEE Transactions on Neural Networks
568:is the i feature of the pattern u:
388:{\displaystyle 0\leq x\leq \theta }
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423:{\displaystyle \Theta (x)=\theta }
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561:{\displaystyle (\alpha _{i}(u))}
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891:= 1 case, the 1-bit content of
526:=1 if i=j and 0 otherwise.)and
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917:{\displaystyle m_{ci\alpha }}
779:{\displaystyle m_{ci\alpha }}
724:{\displaystyle m_{ci\alpha }}
684:{\displaystyle u_{\eta }i(j)}
519:{\displaystyle \delta _{i,j}}
482:{\displaystyle \delta _{i,j}}
449:{\displaystyle x\geq \theta }
1356:Fukunaga, Keinosuke (1990).
1229:10.1007/978-0-387-84858-7_14
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924:is set if any pattern of D
356:{\displaystyle \Theta (x)}
1118:Artificial Neural Network
40:This article includes a
1339:N-Tuple Neural Networks
1070:one-bit word RAMs with
941:{\displaystyle \alpha }
884:{\displaystyle \theta }
744:{\displaystyle \alpha }
160:{\displaystyle \Theta }
69:more precise citations.
1251:Sejnowski, Terrence J.
1198:: CS1 maint: others (
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42:list of references
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1238:978-0-387-84857-0
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16:(Redirected from
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1331:Further reading
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61:Please help
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67:introducing
1154:References
102:algorithms
1259:MIT Press
1194:cite book
1186:553956424
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259:δ
244:∈
237:∑
230:Θ
210:∑
155:Θ
112:Algorithm
75:June 2018
1407:Category
1303:18267737
1112:See also
363:= x for
696:RAMnet.
489:is the
330:where D
98:RAMnets
63:improve
1387:
1368:
1345:
1326:(2009)
1301:
1265:
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648:Here u
48:, or
1385:ISBN
1366:ISBN
1343:ISBN
1317:link
1299:PMID
1263:ISBN
1233:ISBN
1200:link
1182:OCLC
1172:ISBN
430:for
1291:doi
1225:doi
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