Download Artificial Neural Networks and Machine Learning – ICANN by Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo PDF

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

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

Show description

Read or Download Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings PDF

Similar networks books

802.11ac: A Survival Guide

The following frontier for instant LANs is 802. 11ac, a typical that raises throughput past one gigabit in step with moment. This concise advisor presents in-depth details that will help you plan for 802. 11ac, with technical information on layout, community operations, deployment, and monitoring.

Author Matthew Gast—an specialist who led the advance of 802. 11-2012 and defense activity teams on the wireless Alliance—explains how 802. 11ac won't in simple terms elevate the rate of your community, yet its capability besides. even if you must serve extra consumers together with your present point of throughput, or serve your latest buyer load with greater throughput, 802. 11ac is the answer. This publication will get you started.

know how the 802. 11ac protocol works to enhance the rate and capability of a instant LAN
discover how beamforming raises pace ability by means of bettering hyperlink margin, and lays the root for multi-user MIMO
find out how multi-user MIMO raises ability through allowing an AP to ship info to a number of consumers concurrently
Plan while and the way to improve your community to 802. 11ac by way of comparing shopper units, functions, and community connections

Phylogenetic networks

The evolutionary historical past of species is generally represented utilizing a rooted phylogenetic tree. besides the fact that, while reticulate occasions reminiscent of hybridization, horizontal gene move or recombination are believed to be concerned, phylogenetic networks that could accommodate non-treelike evolution have an immense function to play.

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

Example text

Blue line is robot trajectory during on-line control and learning. 2 0 0 100 200 300 400 500 time Fig. 3. ESN predictions (J) in comparison with utility function (U) during the all training course in parallel with time varying parameter γ 30 P. Koprinkova-Hristova J U 6 J, U 5 4 3 2 1 0 -1 -2 -3 -4 0 500 1000 time 1500 Fig. 4. 5 -2 0 500 1000 time 1500 2000 2500 Fig. 5. Predictions of ESN critic trained with IP tuning of reservoir Next we investigated the predictions of ESN critic in comparison with utility function in the case when IP tuning is not involved (Fig.

In the present study, first we demonstrate the learning results for a conventional CTRNN in which the abovementioned 12 fluctuating Lissajous curves were reused as training data. Note that the learning results for an S-CTRNN have already been published in [12]. This study revisits the results for S-CTRNN and compares them with the learning results for CTRNN in order to clarify the essential differences in learning capabilities between conventional CTRNN and S-CTRNN. In addition, the present paper presents the results of an additional experiment on the recognition of fluctuating temporal patterns using the trained S-CTRNN in order to demonstrate the recognition capabilities of the network, and provides an analysis of a self-organized initial state space of the S-CTRNN that seems to contribute to both its learning and recognition capabilities.

In (b), untrained attractors appeared (such as (1, 2)), and in (c), almost none of the training patterns were learned. (1) (5) (9) × × × Output of conventional CTRNN (2) (6) × × × (10) (a) × (4) (1) (7) (8) (5) (11) (12) (9) (3) × × × Output of conventional CTRNN (2) × (6) × (3) (4) (1) (7) (8) (5) (12) (9) × (10) (11) (b) × × × Output of conventional CTRNN (2) (6) × (3) × (7) (4) × × × (10) (11) (8) × × × (12) (c) Fig. 2. Phase plots of output generated by CTRNNs initialized with different random values.

Download PDF sample

Rated 4.69 of 5 – based on 27 votes