{"product_id":"improving-classifier-generalization-9789811950735","title":"Improving Classifier Generalization","description":"\u003cp\u003eThis 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.\u003c\/p\u003e","brand":"Gardners","offers":[{"title":"Default Title","offer_id":53590368321815,"sku":null,"price":16231.5,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0679\/6918\/8119\/files\/9789811950735.jpg?v=1782729863","url":"https:\/\/payment.letskitaboo.com\/te\/products\/improving-classifier-generalization-9789811950735","provider":"Kitaboo One eStore","version":"1.0","type":"link"}