TAILIEUCHUNG - Chapter 9: Combination of classifiers

Combination of classifiers A combination or an ensemble of classifiers is a set of classifiers are combined to classify new examples. A combination of classifiers is often more accurate than the individual classifiers that make them up. This is Chapter 9: Combination of classifiers. | Chapter 9 Combination of classifiers Assoc. Prof. Dr. Duong Tuan Anh Faculty of Computer Science and Engineering, HCMC Univ. of Technology 3/2015 Outline 1 Introduction 2 Bagging 3 Boosting 4 ROC curves Introduction A combination or an ensemble of classifiers is a set of classifiers are combined to classify new examples. A combination of classifiers is often more accurate than the individual classifiers that make them up. A combination of classifiers is a way of compensating for imperfect classifiers. Each classifier is also called an expert. Bagging and boosting are general techniques that can be applied to classification as well as prediction problems. The methods for constructing ensembles of classifiers include Sub-sampling the training set Bagging and boosting each generate a set of classification models, M1, M2, ,Mk. Voting strategies are used to combine the classifications for a given unknown sample. Figure Bagging Given a set D of d tuples, bagging works as .

• Cơ sở dữ liệu
• Combination of classifiers
• ROC curves
• Boostrap aggregation
• Algorithm Bagging
• Difference between bagging and boosting
• Algorithm Adaboost
• Asymmetric error
• Asymmetric boosting
• Imbalanced classification
• Rademacher complexity
• Sai số phân loại bất đối xứng
• Bounding an asymmetric error
• Face detection
• Gaussian distribution
• Based adaboost algorithm
• Haar like pattern
• Weak classifier
• báo cáo hóa học
• công trình nghiên cứu về hóa học
• tài liệu về hóa học
• cách trình bày báo cáo
• Boosted Gaussian Naïve Bayes
• Naïve Bayes
• Data bases
• Networks management
• Naïve Bayes classier
• Adaboost algorithm