Adaptive and Natural Computing Algorithms: 9th International - download pdf or read online

By Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, Bartlomiej Beliczynski (eds.)

ISBN-10: 3642049206

ISBN-13: 9783642049200

This booklet constitutes the completely refereed post-proceedings of the ninth foreign convention on Adaptive and ordinary Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009.

The sixty three revised complete papers awarded have been conscientiously reviewed and chosen from a complete of 112 submissions. The papers are geared up in topical sections on impartial networks, evolutionary computation, studying, delicate computing, bioinformatics in addition to applications.

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Extra resources for Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers

Example text

G. to perform prediction or classification. Thus, in general M : RN → RC , while D ⊂ RN and n ≤ N . In particular all attributes can miss some values. In the latter case n = N . In case of classification the number of output signals produced by model M is C = 1. In our test case the model is implemented using multilayer perceptron. Moreover, eM (D) denotes the mean absolute error of model M on data set D. Furthermore DV is the data set created from D by filling in all the missing values using methods from vector V .

For this paper though, we use a non-evolving, fully connected MLP, and use the more traditional gradient-based training at the core. Early stopping will be involved, in a way. In Sect. 2 we motivate and describe a heuristic training algorithm that operates using the ideas of cross-validation, brute-force parameter search, regularization, and early stopping. To our knowledge, the method as such has not been published before. In the crowded field of machine learning we can be mistaken, in which case we hope this paper is still useful as a utility assessment and computational experiment on that specific heuristic.

After all the N_restarts runs, reload the so-far best combination: beta = beta_best W = W_best 4. Train the final network using the full training data, starting from W_best, using beta_best, tight accuracy demand, many iterations. 3 Gradient-Based Local Minimization To train the MLP classifier further on each step of the algorithm we use a fast gradient-based local minimization scheme based on the conjugate gradient method. We recall from [3] the layer-wise matrix representation of the gradient of Eq.

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Adaptive and Natural Computing Algorithms: 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers by Adrian Horzyk (auth.), Mikko Kolehmainen, Pekka Toivanen, Bartlomiej Beliczynski (eds.)


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