图 书 馆开课单位--巴里大学
1 1/1
1Preference Learning in Recommender Systems[推荐系统中的偏好学习]
Leo Iaquinta(巴里大学) Preference Learning in Recommender Systems
热度：32
Leo Iaquinta(巴里大学) Preference Learning in Recommender Systems
热度：32
2Network Regression with Predictive Clustering Trees[网络与回归预测树聚类]
Daniela Stojanova, Michelangelo Ceci(巴里大学) Regression inference in network data is a challenging task in machine learning and data mining. Network data describe entities represented by nodes, w...
热度：97
Daniela Stojanova, Michelangelo Ceci(巴里大学) Regression inference in network data is a challenging task in machine learning and data mining. Network data describe entities represented by nodes, w...
热度：97
3Discovering Temporal Bisociations for Linking Concepts over Time[随着时间的推移发现连接概念的时间分支]
Corrado Loglisci(巴里大学) Bisociations represent interesting relationships between seemingly unconnected concepts from two or more contexts. Most of the existing approaches tha...
热度：67
Corrado Loglisci(巴里大学) Bisociations represent interesting relationships between seemingly unconnected concepts from two or more contexts. Most of the existing approaches tha...
热度：67
4Conceptual Clustering and its Application to Concept Drift and Novelty Detection[概念聚类及其在概念漂移和新奇检测中的应用]
Claudia d Amato(巴里大学) 概念聚类及其在概念漂移和新奇检测中的应用
热度：42
Claudia d Amato(巴里大学) 概念聚类及其在概念漂移和新奇检测中的应用
热度：42
5Discovering Emerging Patterns in Spatial Databases: a Multi-Relational Approach[发现空间数据库中的新兴模式：多关系方法]
Michelangelo Ceci(巴里大学) 发现空间数据库中的新兴模式：多关系方法
热度：43
Michelangelo Ceci(巴里大学) 发现空间数据库中的新兴模式：多关系方法
热度：43
6Spatial Data Mining Querie language in a GIS System[GIS系统中的空间数据挖掘查询语言]
Annalisa Appice(巴里大学) The strength of GIS is in providing a rich data infrastructure for combining disparate data in meaningful ways by using a spatial arrangement (e.g., ...
热度：88
Annalisa Appice(巴里大学) The strength of GIS is in providing a rich data infrastructure for combining disparate data in meaningful ways by using a spatial arrangement (e.g., ...
热度：88
7Learning from Labeled and Unlabelled Data: When the Smoothness Assumption Holds[从标记和未标记的数据中学习：保持平稳性假设时]
Michelangelo Ceci(巴里大学) During recent years, there has been a growing interest in learning algorithms capable of utilizing both labeled and unlabeled data for prediction task...
热度：35
Michelangelo Ceci(巴里大学) During recent years, there has been a growing interest in learning algorithms capable of utilizing both labeled and unlabeled data for prediction task...
热度：35
8Mining Relational Model Trees[挖掘关系模型树]
Annalisa Appice(巴里大学) Multi-Relational Data Mining (MRDM) refers to the process of discovering implicit, previously unknown and potentially useful information from data sca...
热度：37
Annalisa Appice(巴里大学) Multi-Relational Data Mining (MRDM) refers to the process of discovering implicit, previously unknown and potentially useful information from data sca...
热度：37
9Leave-one-out prediction error as a diagnostic tool[排除一个预测错误作为诊断工具 ]
Sebino Stramaglia(巴里大学 ) We consider here predictability of Systolic Blood Pressure (SAP) time series under paced respiration (Akselrod et al 1985), and show that a suitable i...
热度：69
Sebino Stramaglia(巴里大学 ) We consider here predictability of Systolic Blood Pressure (SAP) time series under paced respiration (Akselrod et al 1985), and show that a suitable i...
热度：69
10Statistical Learning for Inductive Query Answering on OWL Ontologies[OWL本体归纳查询应答的统计学习]
Claudia d'Amato(巴里大学) A novel family of parametric language-independent kernel functions defined for individuals within ontologies is presented. They are easily integrated ...
热度：60
Claudia d'Amato(巴里大学) A novel family of parametric language-independent kernel functions defined for individuals within ontologies is presented. They are easily integrated ...
热度：60
1 1/1