开课单位--巴里大学
1 1/1
1
Preference 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:29
Leo Iaquinta(巴里大学) Preference Learning in Recommender Systems
热度:29
2
Network 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:96
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:96
3
Discovering 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:57
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:57
4
Conceptual 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:41
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:41
5
Discovering 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
6
Spatial 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:83
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:83
7
Learning 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
8
Mining 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
9
Leave-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:67
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:67
10
Statistical 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:59
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:59
1 1/1