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95 lines
4.1 KiB
Plaintext
Executable File
95 lines
4.1 KiB
Plaintext
Executable File
NOTE: all files associated to this .names file have the ann- prefix.
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1. Title: Thyroid Domain
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2. Sources:
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(a) Donors: Randolf Werner
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evol@uniko.uni-koblenz.de
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(b) Obtained from Daimler-Benz.
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(c) Date: October 1992
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3. Past Usage:
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(a) "Optimization of the Backpropagation Algorithm for Training Multilayer
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Perceptrons":
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ftp archive.cis.ohio-state.edu or ftp 128.146.8.52
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cd pub/neuroprose
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binary
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get schiff.bp_speedup.ps.Z
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quit
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The report is an overview of many different backprop speedup techniques.
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15 different algorithms are described in detail and compared by using
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a big, very hard to solve, practical data set. Learning speed and network
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classification performance with respect to the training data set and also
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with respect to a testing data set are discussed.
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These are the tested algorithms:
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backprop
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backprop (batch mode)
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backprop + Learning rate calculated by Eaton and Oliver's formula
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backprop + decreasing learning rate (Darken and Moody)
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backprop + Learning rate adaptation for each training pattern (J. Schmidhuber)
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backprop + evolutionarily learning rate adaptation (R. Salomon)
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backprop + angle driven learning rate adaptation(Chan and Fallside)
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Polak-Ribiere + line search (Kramer and Vincentelli)
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Conj. gradient + line search (Leonard and Kramer)
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backprop + learning rate adaptation by sign changes (Silva and Almeida)
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SuperSAB (T. Tollenaere)
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Delta-Bar-Delta (Jacobs)
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RPROP (Riedmiller and Braun)
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Quickprop (Fahlman)
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Cascade correlation (Fahlman)
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(b) "Synthesis and Performance Analysis of Multilayer eural Network Architectures":
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ftp archive.cis.ohio-state.edu or ftp 128.146.8.52
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cd pub/neuroprose
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binary
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get schiff.gann.ps.Z
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quit
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In this paper we present various approaches for automatic topology-optimization
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of backpropagation networks. First of all, we review the basics of genetic
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algorithms which are our essential tool for a topology search. Then we give a
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survey of backprop and the topological properties of feedforward networks. We
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report on pioneer work in the filed of topology--optimization. Our first
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approach was based on evolutions strategies which used only mutation to change
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the parent's topologies. Now, we found a way to extend this approach by an
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crossover operator which is essential to all genetic search methods.
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In contrast to competing approaches it allows that two parent networks with
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different number of units can mate and produce a (valid) child network, which
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inherits genes from both of the parents. We applied our genetic algorithm to a
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medical classification problem which is extremly difficult to solve. The
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performance with respect to the training set and a test set of pattern samples
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was compared to fixed network topologies. Our results confirm that the topology
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optimization makes sense, because the generated networks outperform the fixed
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topologies and reach classification performances near optimum.
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4. Relevant Information:
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The problem is to determine whether a patient referred to the clinic is
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hypothyroid. Therefore three classes are built: normal (not hypothyroid),
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hyperfunction and subnormal functioning. Because 92 percent of the patients
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are not hyperthyroid a good classifier must be significant better than 92%.
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Note
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These are the attributes Quinlans used in the case study of his article
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"Simplifying Decision Trees" (International Journal of Man-Machine Studies
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(1987) 221-234). Unfortunately this data differ from the version already
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present (donated by Ross Quinlan) I (Randolf Werner) don't know any more
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details about the dataset. But it's hard to train Backpropagation ANNs with
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it. The dataset is used in two technical reports (see above).
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5. Number of Instances: ann-train.data: 3772, ann-test.data: 3428
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6. Number of Classes: 3
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7. Number of Attributes: 21 (15 attributes are binary,
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6 attributes are continuous)
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