By Ignacio Rojas, Gonzalo Joya, Joan Cabestany
This two-volume set LNCS 7902 and 7903 constitutes the refereed lawsuits of the twelfth overseas Work-Conference on synthetic Neural Networks, IWANN 2013, held in Puerto de l. a. Cruz, Tenerife, Spain, in June 2013. The 116 revised papers have been rigorously reviewed and chosen from a number of submissions for presentation in volumes. The papers discover sections on mathematical and theoretical equipment in computational intelligence, neurocomputational formulations, studying and variation emulation of cognitive features, bio-inspired structures and neuro-engineering, complicated issues in computational intelligence and purposes
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Extra resources for Advances in Computational Intelligence: 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Proceedings, Part 1
A computer directed to work deeply on a problem, while regularly checking for pattern changes in an outside data stream would be able to do so in two ways: true multitasking if more than one processor were available, or taskswitching. Task-switching is when a computer goes back and forth between multiple tasks, often at a speed that gives the impression of multitasking. A human directed to do the same thing would have a much harder time of it. A. Brown We can only multi-task if each task uses a different type of processor – like walking and chewing gum at the same time, or talking while knitting.
Task-switching is when a computer goes back and forth between multiple tasks, often at a speed that gives the impression of multitasking. A human directed to do the same thing would have a much harder time of it. A. Brown We can only multi-task if each task uses a different type of processor – like walking and chewing gum at the same time, or talking while knitting. Real multi-tasking, say reading a book while carrying on a conversation, is beyond our normal range of capabilities. We can fake it, but we would really be task-switching, which is what we’ve all experienced when talking with someone who is also reading, composing or sending a text message at the same time.
Data SLFN Construction using ELM Ranking of the best neurons by LARS Selection of the optimal number of neurons by LOO 21 Model Fig. 2. Illustration of the three OP-ELM steps: the SLFN is first built using the ELM approach (random initialization of internal weights and biases); then a LARS algorithm is used to rank the neurons of the hidden layer; finally the selection of the optimal number of neurons for the OP-ELM model is performed using a Leave-One-Out criterion The idea is to build a wrapper around the original ELM, with a neuron pruning strategy.