Current Search:   Dingding, Wang (x)

View All Items

  • CSV Spreadsheet
(1 - 3 of 3)
Automatic classification of communication logs into implementation stages via text analysis
Title
Automatic classification of communication logs into implementation stages via text analysis.
Creator
Wang, Dingding, Ogihara, Mitsunori, Gallo, Carlos, Villamar, Juan A., Smith, Justin D., Vermeer, Wouter, Cruden, Gracelyn, Benbow, Nanette, Brown, C. Hendricks
Date Issued
2015-12-06
PURL
http://purl.flvc.org/fau/fd/FAUIR000198
Format
Citation
META-LEARNING AND ENSEMBLE METHODS FOR DEEP NEURAL NETWORKS
Title
META-LEARNING AND ENSEMBLE METHODS FOR DEEP NEURAL NETWORKS.
Creator
Liu, Feng, Dingding, Wang, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Deep Neural Networks have been widely applied in many different applications and achieve significant improvement over classical machine learning techniques. However, training a neural network usually requires large amount of data, which is not guaranteed in some applications such as medical image classification. To address this issue, people propose to implement meta learning and ensemble learning techniques to make deep learning trainers more powerful. This thesis focuses on using deep...
Show moreDeep Neural Networks have been widely applied in many different applications and achieve significant improvement over classical machine learning techniques. However, training a neural network usually requires large amount of data, which is not guaranteed in some applications such as medical image classification. To address this issue, people propose to implement meta learning and ensemble learning techniques to make deep learning trainers more powerful. This thesis focuses on using deep learning equipped with meta learning and ensemble learning to study specific problems. We first propose a new deep learning based method for suggestion mining. The major challenges of suggestion mining include cross domain issue and the issues caused by unstructured and highly imbalanced data structure. To overcome these challenges, we propose to apply Random Multi-model Deep Learning (RMDL) which combines three different deep learning architectures (DNNs, RNNs and CNNs) and automatically selects the optimal hyper parameter to improve the robustness and flexibility of the model. Our experimental results on the SemEval-2019 competition Task 9 data sets demonstrate that our proposed RMDL outperforms most of the existing suggestion mining methods.
Show less
Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013481
Subject Headings
Neural networks (Computer science), Deep learning, Neural Networks in Applications, Machine learning--Technique
Format
Document (PDF)
Machine Learning Methods to Understand Textual Data
Title
Machine Learning Methods to Understand Textual Data.
Creator
Sohangir, Sahar, Wang, Dingding, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
The amount of textual data that produce every minute on the internet is extremely high. Processing of this tremendous volume of mostly unstructured data is not a straightforward function. But the enormous amount of useful information that lay down on them motivate scientists to investigate efficient and effective techniques and algorithms to discover meaningful patterns. Social network applications provide opportunities for people around the world to be in contact and share their valuable...
Show moreThe amount of textual data that produce every minute on the internet is extremely high. Processing of this tremendous volume of mostly unstructured data is not a straightforward function. But the enormous amount of useful information that lay down on them motivate scientists to investigate efficient and effective techniques and algorithms to discover meaningful patterns. Social network applications provide opportunities for people around the world to be in contact and share their valuable knowledge, such as chat, comments, and discussion boards. People usually do not care about spelling and accurate grammatical construction of a sentence in everyday life conversations. Therefore, extracting information from such datasets are more complicated. Text mining can be a solution to this problem. Text mining is a knowledge discovery process used to extract patterns from natural language. Application of text mining techniques on social networking websites can reveal a significant amount of information. Text mining in conjunction with social networks can be used for finding a general opinion about any special subject, human thinking patterns, and group identification. In this study, we investigate machine learning methods in textual data in six chapters.
Show less
Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013107
Subject Headings
Machine learning, Internet--Data processing, Text Mining
Format
Document (PDF)