Get Artificial Neural Networks and Machine Learning – ICANN PDF

By Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)

ISBN-10: 3319111787

ISBN-13: 9783319111780

ISBN-10: 3319111795

ISBN-13: 9783319111797

The publication constitutes the complaints of the twenty fourth foreign convention on synthetic Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014.
The 107 papers incorporated within the complaints have been rigorously reviewed and chosen from 173 submissions. the focal point of the papers is on following themes: recurrent networks; aggressive studying and self-organisation; clustering and class; bushes and graphs; human-machine interplay; deep networks; idea; reinforcement studying and motion; imaginative and prescient; supervised studying; dynamical types and time sequence; neuroscience; and applications.

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Additional resources for Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings

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In the present study, we demonstrate the learning and recognition capabilities of our recently proposed recurrent neural network (RNN) model called stochastic continuous-time RNN (S-CTRNN). SCTRNN can learn to predict not only the mean but also the variance of the next state of the learning targets. The network parameters consisting of weights, biases, and initial states of context neurons are optimized through maximum likelihood estimation (MLE) using the gradient descent method. First, we clarify the essential difference between the learning capabilities of conventional CTRNN and S-CTRNN by analyzing the results of a numerical experiment in which multiple fluctuating temporal patterns were used as training data, where the variance of the Gaussian noise varied among the patterns.

The number of CG iterations per update was limited to 100. The gradient was computed using the full training set and the curvature was estimated using 10 000 examples. Figure 2(b) depicts the performance of the FTRNN with and without regularization. The performance metric is the average MSE per state component per predicted time step of the best model among five runs, determined by the validation set error. At the beginning of each run, a new parameter initialization was sampled at random. The experiments on the mountain car simulation revealed superior performance of the regularized FTRNN over the plain FTRNN for |DT,2 | ∈ {312, 156}.

A-Scan based lung tumor tissue classification with bidirectional long short term memory networks. In: 2013 IEEE International Workshop on Machine Learning for Signal Processing, MLSP (2013) 10. : Can we build language-independent OCR using LSTM networks? In: Proceedings of the 4th International Workshop on Multilingual OCR, p. 9. ACM (2013) Learning and Recognition of Multiple Fluctuating Temporal Patterns Using S-CTRNN Shingo Murata1 , Hiroaki Arie2 , Tetsuya Ogata2 , Jun Tani3 , and Shigeki Sugano1 1 Department of Modern Mechanical Engineering, Waseda University, Tokyo, Japan 2 Department of Intermedia Art and Science, Waseda University, Tokyo, Japan 3 Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea Abstract.

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Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings by Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)


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