Titlebook: Intelligent Data Engineering and Automated Learning – IDEAL 2019; 20th International C Hujun Yin,David Camacho,Richard Allmendinger Confere

乳缽

書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019影響因子(影響力)




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019影響因子(影響力)學(xué)科排名




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019網(wǎng)絡(luò)公開度




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019被引頻次




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019被引頻次學(xué)科排名




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019年度引用




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019年度引用學(xué)科排名




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019讀者反饋




書目名稱Intelligent Data Engineering and Automated Learning – IDEAL 2019讀者反饋學(xué)科排名

抵押貸款

Scalable Dictionary Classifiers for Time Series Classification,ces in a time series. The ensemble based Bag of Symbolic Fourier Approximation Symbols (BOSS) was found to be a top performing TSC algorithm in a recent evaluation, as well as the best performing dictionary based classifier. However, BOSS does not scale well. We evaluate changes to the way BOSS choo

Seminar

上腭

媒介

Classifying Prostate Histological Images Using Deep Gaussian Processes on a New Optical Density Grae cancer using eosin and hematoxylin stained histopathological images. In this work the above problem is approached as follows: the optical density of each whole slide image is calculated and its eosin and hematoxylin concentration components estimated. Then, hand-crafted features, which are expecte

偏狂癥

強(qiáng)壯

Using Deep Learning for Ordinal Classification of Mobile Marketing User Conversion,o measure the value of product sales when an user clicks an ad. As a case study, we consider big data provided by a global mobile marketing company. Several experiments were held, considering a rolling window validation, different datasets, learning methods and performance measures. Overall, competi

確保

RADE

,Adaptive Dimensionality Adjustment for Online “Principal Component Analysis”,l growth in available data and the resulting storage requirements are often underestimated bottlenecks. Therefore, efficient dimensionality reduction gets more attention and becomes more relevant. One of the most widely used techniques for dimensionality reduction is “Principal Component Analysis” (

山崩

Relevance Metric for Counterfactuals Selection in Decision Trees,herent rationale behind model predictions. In the particular case of example-based explanation methods, they are focused on using particular instances, previously defined or created, to explain the behaviour of models or predictions. Counterfactual-based explanation is one of these methods. A counte