A multimodal data mining framework for soccer goal detection based on decision tree logic

2006 ◽  
Vol 27 (4) ◽  
pp. 312 ◽  
Author(s):  
Shu Ching Chen ◽  
Mei Ling Shyu ◽  
Chengcui Zhang ◽  
Min Chen
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Multimodal Data ◽  
Goal Detection ◽  
Multimodal Data Mining

A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database

2011 ◽  
pp. 170-193
Author(s):  
Zhen Guo ◽  
Christos Faloutsos ◽  
Zhongfei (Mark) Zhang ◽  
Zhongfei (Mark) Zhang
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Multiple Instance Learning ◽  
Multimedia Database ◽  
Multimodal Data ◽  
Learning Framework ◽  
New Information ◽  
Database Indexing ◽  
Constant Operation ◽  
Multimodal Data Mining

This chapter presents a highly scalable and adaptable co-learning framework on multimodal data mining in a multimedia database. The co-learning framework is based on the multiple instance learning theory. The framework enjoys a strong scalability in the sense that the query time complexity is a constant, independent of the database scale, and the mining effectiveness is also independent of the database scale, allowing facilitating a multimodal querying to a very large scale multimedia database. At the same time, this framework also enjoys a strong adaptability in the sense that it allows incrementally updating the database indexing with a constant operation when the database is dynamically updated with new information. Hence, this framework excels many of the existing multimodal data mining methods in the literature that are neither scalable nor adaptable at all. Theoretic analysis and empirical evaluations are provided to demonstrate the advantage of the strong scalability and adaptability. While this framework is general for multimodal data mining in any specific domains, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance. They have compared the framework with a state-of-the-art multimodal data mining method to demonstrate the effectiveness and the promise of the framework.

A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database

Data Mining ◽  
2013 ◽  
pp. 567-586
Author(s):  
Zhongfei (Mark) Zhang ◽  
Zhen Guo ◽  
Christos Faloutsos ◽  
Jia-Yu Pan
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Multiple Instance Learning ◽  
Multimedia Database ◽  
Multimodal Data ◽  
Learning Framework ◽  
New Information ◽  
Database Indexing ◽  
Constant Operation ◽  
Multimodal Data Mining

This chapter presents a highly scalable and adaptable co-learning framework on multimodal data mining in a multimedia database. The co-learning framework is based on the multiple instance learning theory. The framework enjoys a strong scalability in the sense that the query time complexity is a constant, independent of the database scale, and the mining effectiveness is also independent of the database scale, allowing facilitating a multimodal querying to a very large scale multimedia database. At the same time, this framework also enjoys a strong adaptability in the sense that it allows incrementally updating the database indexing with a constant operation when the database is dynamically updated with new information. Hence, this framework excels many of the existing multimodal data mining methods in the literature that are neither scalable nor adaptable at all. Theoretic analysis and empirical evaluations are provided to demonstrate the advantage of the strong scalability and adaptability. While this framework is general for multimodal data mining in any specific domains, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance. They have compared the framework with a state-of-the-art multimodal data mining method to demonstrate the effectiveness and the promise of the framework.

A Multiple-Instance Learning Based Approach to Multimodal Data Mining

2012 ◽  
pp. 124-142
Author(s):  
Zhongfei (Mark) Zhang ◽  
Zhen Guo ◽  
Jia-Yu Pan
Keyword(s):  
Data Mining ◽  
State Of The Art ◽  
Image Database ◽  
Multiple Instance Learning ◽  
Multimedia Database ◽  
Theoretic Analysis ◽  
Specific Domain ◽  
Multimodal Data ◽  
Learning Framework ◽  
Multimodal Data Mining

This paper presents multiple-instance learning based approach to multimodal data mining in a multimedia database. This approach is a highly scalable and adaptable framework that the authors call co-learning. Theoretic analysis and empirical evaluations demonstrate the advantage of the strong scalability and adaptability. Although this framework is general for multimodal data mining in any specific domain, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance in comparison with a state-of-the-art multimodal data mining method to showcase the promise of the co-learning framework.

Multimodal Data Mining in a Multimedia Database Based on Structured Max Margin Learning

2016 ◽  
Vol 10 (3) ◽  
pp. 1-30 ◽  
Author(s):  
Zhen Guo ◽  
Zhongfei (Mark) Zhang ◽  
Eric P. Xing ◽  
Christos Faloutsos
Keyword(s):  
Data Mining ◽  
Multimedia Database ◽  
Multimodal Data ◽  
Multimodal Data Mining

scholarly journals Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 153072-153082
Author(s):  
Urooba Sehar ◽  
Summrina Kanwal ◽  
Kia Dashtipur ◽  
Usama Mir ◽  
Ubaid Abbasi ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Multimodal Data ◽  
Multimodal Data Mining
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