Titlebook: Artificial Neural Networks and Machine Learning -- ICANN 2014; 24th International C Stefan Wermter,Cornelius Weber,Alessandro E. P. Vi Conf

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書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014影響因子(影響力)




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014影響因子(影響力)學科排名




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014網(wǎng)絡(luò)公開度




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014網(wǎng)絡(luò)公開度學科排名




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014被引頻次




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014被引頻次學科排名




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014年度引用




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014年度引用學科排名




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014讀者反饋




書目名稱Artificial Neural Networks and Machine Learning -- ICANN 2014讀者反饋學科排名

LATHE

Human Action Recognition with Hierarchical Growing Neural Gas Learningm depth map sequences. We then cluster pose-motion cues with a two-stream hierarchical architecture based on growing neural gas (GNG). Multi-cue trajectories are finally combined to provide prototypical action dynamics in the joint feature space. We extend the unsupervised GNG with two labelling fun

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Real-Time Anomaly Detection with a Growing Neural Gasnchronously operating pixels that is inspired by the human retina. Each pixel reports events of illumination changes, are processed in a purely event-based tracker that pursues edges of events in the input stream. The tracker estimates are used to determine whether the input events originate from an

倔強一點

盡忠

A Non-parametric Maximum Entropy Clustering quantities such as entropy and mutual information. Recently, since these quantities can be estimated in non-parametric manner, non-parametric information theoretic clustering gains much attention. Assuming the dataset is sampled from a certain cluster, and assigning different sampling weights depen

解脫

壁畫

PLIC

Leaving Local Optima in Unsupervised Kernel Regressione local optima in the data space reconstruction error (DSRE) minimization process of unsupervised kernel regression (UKR). For this sake, we concentrate on a hybrid UKR variant that combines iterative solution construction with gradient descent based optimization. Patterns with high reconstruction e

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