Titlebook: Artificial Neural Networks and Machine Learning -- ICANN 2012; 22nd International C Alessandro E. P. Villa,W?odzis?aw Duch,Günther Pal Conf

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書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2012影響因子(影響力)學(xué)科排名




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書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2012網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2012被引頻次




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書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2012年度引用學(xué)科排名




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2012讀者反饋




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2012讀者反饋學(xué)科排名

蓋他為秘密

Theoretical Analysis of Function of Derivative Term in On-Line Gradient Descent Learningh as by using the natural gradient, has been proposed for speeding up the convergence. Beside this sophisticated method, ”simple method” that replace the derivative term with a constant has proposed and showed that this greatly increases convergence speed. Although this phenomenon has been analyzed

剛毅

記憶

Goblet-Cells

Electricity Load Forecasting: A Weekday-Based Approachlection using autocorrelation analysis for each day of the week and build a separate prediction model using linear regression and backpropagation neural networks. We used two years of 5-minute electricity load data for the state of New South Wales in Australia to evaluate performance. Our results sh

Meager

Adaptive Exploration Using Stochastic Neuronsl-free temporal-difference learning using discrete actions. The advantage is in particular memory efficiency, because memorizing exploratory data is only required for starting states. Hence, if a learning problem consist of only one starting state, exploratory data can be considered as being global.

Tinea-Capitis

Comparison of Long-Term Adaptivity for Neural Networks. Problems occur if the system dynamics change over time (concept drift). We survey different approaches to handle concept drift and to ensure good prognosis quality over long time ranges. Two main approaches - data accumulation and ensemble learning - are explained and implemented. We compare the c

雪崩

TAP

A Modified Artificial Fish Swarm Algorithm for the Optimization of Extreme Learning Machinesffer from generalization loss caused by overfitting, thereby the process of learning is highly biased. For this work we use Extreme Learning Machine which is an algorithm for training single hidden layer neural networks, and propose a novel swarm-based method for optimizing its weights and improving

Corral