Knowledge Based Systems（KBS ）的VOLUME 106论文列表

卷期号： Volume 106

发布时间： 15 August 2016

卷期年份： 2016

卷期官网： https://www.sciencedirect.com/journal/knowledge-based-systems/vol/106/suppl/C

Editorial Board

GRASP with path relinking for the single row facility layout problem

Temporal landmark graphs for solving overconstrained planning problems

Visual tracking via exemplar regression model

Automatic signal abnormality detection using time-frequency features and machine learning: A newborn EEG seizure case study

Automatic creation of scenarios for evaluating spoken dialogue systems via user-simulation

Near-synonym substitution using a discriminative vector space model

A multi-level approach using genetic algorithms in an ensemble of Least Squares Support Vector Machines

Consensus building in group decision making based on multiplicative consistency with incomplete reciprocal preference relations

A novel quadrature particle filtering based on fuzzy c-means clustering

Grounding the detection of the user’s likes and dislikes on the topic structure of human-agent interactions

Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks

Dynamic labeling scheme for XML updates

A survey of trust management systems for online social communities – Trust modeling, trust inference and attacks

A recommender system of reviewers and experts in reviewing problems

Evidential clustering of large dissimilarity data

Suggest what to tag: Recommending more precise hashtags based on users’ dynamic interests and streaming tweet content

Consensus reaching model in the complex and dynamic MAGDM problem

Learning distributed word representation with multi-contextual mixed embedding

Automatic labelling of clusters of discrete and continuous data with supervised machine learning

Human error tolerant anomaly detection based on time-periodic packet sampling

Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data

Spatially-variant noise filtering in magnetic resonance imaging: A consensus-based approach