Titlebook: Artificial Neural Networks - ICANN 2010; 20th International C Konstantinos Diamantaras,Wlodek Duch,Lazaros S. Il Conference proceedings 201

Fantasy

Analyzing Classification Methods in Multi-label Tasksnnotation of images. This paper presents a comparative analysis of some existing multi-label classification methods applied to different domains. The main aim of this analysis is to evaluate the performance of such methods in different tasks and using different evaluation metrics.

佛刊

Fiber Parameter Studies with the OTDRs has been done, but with cost functions that scale quadratically. Training a bottleneck classifier scales linearly, but still gives results comparable to or sometimes better than two earlier supervised methods.

Ige326

J. J. Mecholsky,S. W. Freiman,S. M. Moreyexpression microarray datasets of different kinds of cancer. A comparative study with other classifiers such as Support Vector Machine (SVM), C4.5, na?ve Bayes and k-Nearest Neighbor is performed. Our approach shows excellent results outperforming all other classifiers.

漫不經(jīng)心

https://doi.org/10.1007/978-3-662-52764-1The quality of the predictor is tested on a large test set of eye movement data and compared with the performance of two state-of-the-art saliency models on this data set. The proposed model demonstrates significant improvement – mean ROC score of 0.665 – over the selected baseline models with ROC scores of 0.625 and 0.635.

嗎啡

Ischemia

CHASE

Infant

Deep Bottleneck Classifiers in Supervised Dimension Reductions has been done, but with cost functions that scale quadratically. Training a bottleneck classifier scales linearly, but still gives results comparable to or sometimes better than two earlier supervised methods.

aspect

Local Modeling Classifier for Microarray Gene-Expression Dataexpression microarray datasets of different kinds of cancer. A comparative study with other classifiers such as Support Vector Machine (SVM), C4.5, na?ve Bayes and k-Nearest Neighbor is performed. Our approach shows excellent results outperforming all other classifiers.

有法律效應(yīng)

A Learned Saliency Predictor for Dynamic Natural ScenesThe quality of the predictor is tested on a large test set of eye movement data and compared with the performance of two state-of-the-art saliency models on this data set. The proposed model demonstrates significant improvement – mean ROC score of 0.665 – over the selected baseline models with ROC scores of 0.625 and 0.635.