Improving Classifier Generalization

Improving Classifier Generalization
Title Improving Classifier Generalization PDF eBook
Author Rahul Kumar Sevakula
Publisher
Pages 0
Release 2023
Genre
ISBN 9789811950742

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This book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repository to predictive maintenance problems and cancer classification problems. The book specifically provides a detailed tutorial on how to approach time-series classification problems and discusses two real time case studies on condition monitoring. In addition to describing the various aspects a data scientist must consider before finalizing their approach to a classification problem and reviewing the state of the art for improving classification generalization performance, it also discusses in detail the authors own contributions to the field, including MVPC - a classifier with very low VC dimension, a graphical indices based framework for reliable predictive maintenance and a novel general-purpose membership functions for Fuzzy Support Vector Machine which provides state of the art performance with noisy datasets, and a novel scheme to introduce deep learning in Fuzzy Rule based classifiers (FRCs). This volume will serve as a useful reference for researchers and students working on machine learning, health monitoring, predictive maintenance, time-series analysis, gene-expression data classification. .

Improving Classifier Generalization

Improving Classifier Generalization
Title Improving Classifier Generalization PDF eBook
Author Rahul Kumar Sevakula
Publisher Springer Nature
Pages 181
Release 2022-09-29
Genre Computers
ISBN 9811950733

Download Improving Classifier Generalization Book in PDF, Epub and Kindle

This book elaborately discusses techniques commonly used to improve generalization performance in classification approaches. The contents highlight methods to improve classification performance in numerous case studies: ranging from datasets of UCI repository to predictive maintenance problems and cancer classification problems. The book specifically provides a detailed tutorial on how to approach time-series classification problems and discusses two real time case studies on condition monitoring. In addition to describing the various aspects a data scientist must consider before finalizing their approach to a classification problem and reviewing the state of the art for improving classification generalization performance, it also discusses in detail the authors own contributions to the field, including MVPC - a classifier with very low VC dimension, a graphical indices based framework for reliable predictive maintenance and a novel general-purpose membership functions for Fuzzy Support Vector Machine which provides state of the art performance with noisy datasets, and a novel scheme to introduce deep learning in Fuzzy Rule based classifiers (FRCs). This volume will serve as a useful reference for researchers and students working on machine learning, health monitoring, predictive maintenance, time-series analysis, gene-expression data classification.

Computer Networks and Intelligent Computing

Computer Networks and Intelligent Computing
Title Computer Networks and Intelligent Computing PDF eBook
Author K. R. Venugopal
Publisher Springer
Pages 701
Release 2011-07-20
Genre Computers
ISBN 3642227864

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This book constitutes the refereed proceedings of the 5th International Conference on Information Processing, ICIP 2011, held in Bangalore, India, in August 2011. The 86 revised full papers presented were carefully reviewed and selected from 514 submissions. The papers are organized in topical sections on data mining; Web mining; artificial intelligence; soft computing; software engineering; computer communication networks; wireless networks; distributed systems and storage networks; signal processing; image processing and pattern recognition.

Improving the Generalization Ability of Neural Network Classifiers

Improving the Generalization Ability of Neural Network Classifiers
Title Improving the Generalization Ability of Neural Network Classifiers PDF eBook
Author Kailash L. Kalantri
Publisher
Pages 146
Release 1992
Genre Neural networks (Computer science)
ISBN

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Generalization With Deep Learning: For Improvement On Sensing Capability

Generalization With Deep Learning: For Improvement On Sensing Capability
Title Generalization With Deep Learning: For Improvement On Sensing Capability PDF eBook
Author Zhenghua Chen
Publisher World Scientific
Pages 327
Release 2021-04-07
Genre Computers
ISBN 9811218854

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Deep Learning has achieved great success in many challenging research areas, such as image recognition and natural language processing. The key merit of deep learning is to automatically learn good feature representation from massive data conceptually. In this book, we will show that the deep learning technology can be a very good candidate for improving sensing capabilities.In this edited volume, we aim to narrow the gap between humans and machines by showcasing various deep learning applications in the area of sensing. The book will cover the fundamentals of deep learning techniques and their applications in real-world problems including activity sensing, remote sensing and medical sensing. It will demonstrate how different deep learning techniques help to improve the sensing capabilities and enable scientists and practitioners to make insightful observations and generate invaluable discoveries from different types of data.

Harnessing Unlabeled Data for Improving Generalization of Deep Learning Methods

Harnessing Unlabeled Data for Improving Generalization of Deep Learning Methods
Title Harnessing Unlabeled Data for Improving Generalization of Deep Learning Methods PDF eBook
Author Deepika Shanmugasundaram
Publisher
Pages 0
Release 2023
Genre Electronic dissertations
ISBN

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Recent advancements in Deep Learning, Artificial Intelligence, and Computer Vision have reached a critical stage, enabling researchers to explore the automatic extraction of individual demographic traits, known as soft-biometrics. This research aims to leverage unlabeled data in predicting soft-biometric traits, such as gender and age, using deep learning models. The objective is to develop a model that can accurately classify these traits by utilizing semi-supervised methods that rely on a limited amount of labeled data and a vast amount of unlabeled data. While unlabeled data may initially seem devoid of crucial information, this thesis explores how it can be effectively used to enhance classification accuracy, especially in scenarios where labeled data is scarce. This study evaluated the accuracy of different image classification models on the Celeb-A and NIR-VIS datasets using co-training, mix-up procedure, knowledge distillation, and blind distillation techniques. The results showed that incorporating these methods led to improvements in accuracy across both datasets and various attributes such as gender classification and smiling classification. Exploring the combined use of different techniques and investigating their synergistic effects could lead to further accuracy improvements. Evaluating the models on larger and more diverse datasets, analyzing their generalization capabilities, optimizing hyperparameters and architectures, and applying the techniques to other computer vision tasks were also identified as areas for future research.

Multiple Classifier Systems

Multiple Classifier Systems
Title Multiple Classifier Systems PDF eBook
Author Jón Atli Benediktsson
Publisher Springer
Pages 551
Release 2009-06-10
Genre Computers
ISBN 3642023266

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These proceedings are a record of the Multiple Classi?er Systems Workshop, MCS 2009, held at the University of Iceland, Reykjavik, Iceland in June 2009. Being the eighth in a well-established series of meetings providing an inter- tional forum for the discussion of issues in multiple classi?er system design, the workshop achieved its objective of bringing together researchers from diverse communities (neural networks,pattern recognition,machine learning and stat- tics) concerned with this research topic. From more than 70 submissions, the Program Committee selected 54 papers to create an interesting scienti?c program. The special focus of MCS 2009 was on the application of multiple classi?er systems in remote sensing. This part- ular application uses multiple classi?ers for raw data fusion, feature level fusion and decision level fusion. In addition to the excellent regular submission in the technical program, outstanding contributions were made by invited speakers Melba Crawford from Purdue University and Zhi-Hua Zhou of Nanjing Univ- sity. Papers of these talks are included in these workshop proceedings. With the workshop’sapplicationfocusbeingonremotesensing,Prof.Crawford’sexpertise in the use of multiple classi?cation systems in this context made the discussions on this topic at MCS 2009 particularly fruitful.