Titlebook: Discovery Science; 20th International C Akihiro Yamamoto,Takuya Kida,Tetsuji Kuboyama Conference proceedings 2017 Springer International Pu

GUEER

Multi-label Classification Using Random Label Subset Selectionsormation and algorithm adaptation. Methods from the former group transform the dataset to simpler local problems and then use off-the-shelf methods to solve them. Methods from the latter group change and adapt existing methods to directly address this task and provide a global solution. There is no

Ejaculate

改變

Re-training Deep Neural Networks to Facilitate Boolean Concept Extractionolic representations in the form of rule sets are one way to illustrate their behavior as a whole, as well as the hidden concepts they model in the intermediate layers. The main contribution of the paper is to demonstrate how to facilitate rule extraction from a deep neural network by retraining it

Recess

An In-Depth Experimental Comparison of RNTNs and CNNs for Sentence Modelinged to model sentences, however, little is known about their comparative performance on a common ground, across a variety of datasets, and on the same level of optimization. In this paper, we provide such a novel comparison for two popular architectures, Recursive Neural Tensor Networks (RNTNs) and C

花爭(zhēng)吵

ELUC

高深莫測(cè)

Context-Based Abrupt Change Detection and Adaptation for Categorical Data Streamse in an unsupervised setting. This paper introduces a novel context-based algorithm for categorical data, namely .. In this unsupervised method, multiple drift detection tracks are maintained and their votes are combined in order to determine whether a real change has occurred. In this way, change d

多節(jié)

On a New Competence Measure Applied to the Dynamic Selection of Classifiers Ensemblee methods developed. The performance of constructed MC systems was compared against seven state-of-the-art MC systems using 15 benchmark data sets taken from the UCI Machine Learning Repository. The experimental investigations clearly show the effectiveness of the combined multiclassifier system in

殘暴

專(zhuān)橫

0302-9743 gression, label classification, deep learning, feature selection, recommendation system; and knowledge discovery: recommendation system, community detection, pattern mining, misc..978-3-319-67785-9978-3-319-67786-6Series ISSN 0302-9743 Series E-ISSN 1611-3349