T. Kwok and D. Yeung. Constructive algorithms for structure learning in feedforward neural networks for regression problems problems: A survey. IEEE Transactions on Neural Networks, 8(3):630--645, May 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....approaches are, however, computationally expensive, since selecting the neuron to split depends on each neuron s properties, which must be calculated and tracked. There are a number of authors who have surveyed dynamic construction of ANNs, discussing these and other methods (for example [KY97] Cam97] and [AG97] Genetic Algorithms Genetic algorithms are inspired by the evolution of life. Also called evolutionary algorithms, genetic algorithms for learning in ANNs have recently been explored by a large number of researchers (for example[Yao99] MSG99] and [Koz98] This chapter ....

Tin-Yau Kwok and Dit-Yan Yeung. Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans. on Neural Networks, 8(3):630--645, 1997.

....accuracy and rule simplicity (as discussed in Section 1) an appropriate number of hidden units must be determined, and two general approaches have been proposed in the literature. The constructive algorithms start with a few hidden units and add more units as needed to improve network accuracy [5, 6, 7]. The destructive algorithms, on the other hand, start with a large number of hidden units and remove those that are found to be redundant [8] The number of useful input units corresponds to the number of relevant input attributes of the data. Typical algorithms usually start by assigning one ....

....on standard benchmark data (Section 7.1) The second set of experiments measure the complexity and accuracy of the rules extracted by REFANN (Section 7.2) 7. 1 N2PFA Results There have been a number of papers that propose algorithms for constructing and or training neural network for regression [6, 30, 31, 32, 33]. A recent paper by Frank et al. 34] compares the results of naive Bayes methodology for regression to those from other regression methods including linear regression. Test results from 32 problems are reported in the paper. The data sets are available from their website ....

T. Y. Kwok and D. Y. Yeung, \Constructive algorithms for structure learning in feedforward neural networks for regression problems," IEEE Trans. on Neural Networks, vol. 8, no. 3, pp. 630-645, 1997.

....with a single hidden layer, architecture selection boils down to finding appropriate numbers of units in the input and hidden layers. To find an appropriate number of hidden units, constructive algorithms start with a few hidden units and add more units as needed to improve network accuracy [1, 8, 14]. Destructive algorithms, on the other hand, start with a large number of hidden units and remove those that are found to be redundant [11] The number of useful input units correspond to the number of relevant input attributes of the data. Typical algorithms usually start by assigning one input ....

....hidden units. The pruning process is terminated when no unit can be removed without causing the network accuracy on the cross validation set to drop below the prescribed level. While there are several papers that propose algorithms for constructing and or training neural network for regression [6, 8, 9], we have been unable to find a paper that compares the accuracy of neural networks for regression against those of other traditional methods such as statistical regression method. A recent study by Frank et al. 5] on the application of naive Bayes methodology for regression provides us with an ....

Kwok, T.Y. and Yeung, D.Y. (1997) Constructive algorithms for structure learning in feedforward neural IEEE Trans. on Neural Networks, 8 (3),630-645, May 1997.

....motivation for most CNAs, and they can serve well to give a brief overview of what methods have been employed in CNAs and what for reasons. Of course, this overview cannot pretend to be complete; a broader discussion of different techniques along with a taxonomy of a number of CNAs can be found in Kwok and Yeung (1997). The second strand will outline learning theoretic and psychological considerations which have recently led to augmented emphasis on the merit of these approaches for models of cognitive abilities and development. Again, it cannot be complete; Joseph (in preparation) provides a more comprehensive ....

Kwok, T.-Y. and Yeung, D.-Y. (1997). Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks, 8(3), 630--645.

....algorithms. The second approach, which corresponds to constructive algorithms, starts with a small network and then grows additional hidden units and weights until a satisfactory solution is found. Review for pruning algorithms can be found in [t] while that for constructive algorithms in [2] [3], 4] The constructive approach has a number of advantages over the pruning approach. Firstly, for constructive algo rithms, it is straightforward to specify an initial network 1, whereas for pruning algorithms, one does not know in practice how big the initial network should be. Secondly, ....

T.Y. Kwok and D.Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems," 1996, To appear in IEEE Transactions on Neural Networks.

....algorithms. The second approach, which corresponds to constructive algorithms, starts with a small network and then grows additional hidden units and weights until a satisfactory solution is found. Review for pruning algorithms can be found in [1] while that for constructive algorithms in [2] [3], 4] The constructive approach has a number of advantages over the pruning approach. Firstly, for constructive algorithms, it is straightforward to specify an initial network 1 , whereas for pruning algorithms, one does not know in practice how big the initial network should be. Secondly, ....

T.Y. Kwok and D.Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems," 1996, To appear in IEEE Transactions on Neural Networks.

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T. Kwok and D. Yeung. Constructive algorithms for structure learning in feedforward neural networks for regression problems problems: A survey. IEEE Transactions on Neural Networks, 8(3):630--645, May 1999.

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T.-Y. Kwok and D.-Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems,"IEEE Trans. Neural Networks, vol. 8, pp. 630--645, 1997.

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Tin-Yau Kwok and Dit-Yan Yeung. Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems. IEEE Trans. on Neural Networks, vol. 7, pp.1168-1183, Sept. 1996.

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T.-Y. Kwok, D.-Y. Yeung, Constructive algorithms for structure learning in feedforward neural networks for regression problems, IEEE Trans. Neural Networks 8 (1997) 630---645.

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T.-Y. Kwok and D.-Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems,"IEEE Trans. Neural Networks, vol. 8, pp. 630--645, May 1997.

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T. Y. Kwok, D.Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems," IEEE Trans. Neural Networks, vol 8, pp. 448-472, 1997.

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Kwok, T.-Y. and D.-Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems", IEEE Transactions on Neural Networks, Vol. 8, No. 3, pp. 630--645, 1997.

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Kwok, T.-Y. and D.-Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems", IEEE Transactions on Neural Networks, Vol. 8, No. 3, pp. 630--645, 1997.

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T.Y. Kwok and D.Y. Yeung, Constructive algorithms for structure learning in feedforward neural networks for regression problems, IEEE Transactions on Neural Networks, 8(3), 1997, 630--645.

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T. Y. Kwok and D. Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems," IEEE Trans. Neural Networks, vol. 8, pp. 630--645, May 1997.

No context found.

Tin-Yau Kwok and Dit-Yan Yeung. Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks, 8(3):630--645, 1997.

No context found.

T. Y. Kwok and D. Y. Yeung, "Constructive algorithms for structure learning in feedforward neural networks for regression problems," IEEE Trans. Neural Networks, vol. 8, pp. 630--645, May 1997.

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T. Y. Kwok and D. Y. Yeung. Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks, 8(3):630-645, 1997. 22

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T.Y. Kwok and D.Y. Yeung. Constructive Algorithms for Structure Learning in Feedforward Neural Networks for Regression Problems. In IEEE Transactions on Neural Networks, volume 8, pages 630-- 645, 1997.

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