Meta learning of bounds on the Bayes classifier error
Meta learning uses information from base learners (e.g. classifiers or estimators) as well as information about the learning problem to improve upon the performance of a single base learner. For example, the Bayes error rate of a given feature space, if known, can be used to aid in choosing a classifier, as well as in feature selection and model selection for the base classifiers and the meta classifier. Recent work in the field of f-divergence functional estimation has led to the development of simple and rapidly converging estimators that can be used to estimate various bounds on the Bayes error. We estimate multiple bounds on the Bayes error using an estimator that applies meta learning to slowly converging plug-in estimators to obtain the parametric convergence rate. We compare the estimated bounds empirically on simulated data and then estimate the tighter bounds on features extracted from an image patch analysis of sunspot continuum and magnetogram images.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionMeta-LearningModel SelectionSimilar Papers 제목 키워드 기반
Fast Meta-Learning for Adaptive Hierarchical Classifier Design
We propose a new splitting criterion for a meta-learning approach to multiclass classifier design that adaptively merges the classes into a tree-structured hierarchy of increasingly difficult binary classification proble…
Binary ClassificationClassificationGeneral ClassificationMeta-LearningPAC-Bayes Bounds for Meta-learning with Data-Dependent Prior
By leveraging experience from previous tasks, meta-learning algorithms can achieve effective fast adaptation ability when encountering new tasks. However it is unclear how the generalization property applies to new tasks…
Meta-LearningLearning to Bound the Multi-class Bayes Error
In the context of supervised learning, meta learning uses features, metadata and other information to learn about the difficulty, behavior, or composition of the problem. Using this knowledge can be useful to contextuali…
Meta-LearningOn PAC-Bayesian Bounds for Random Forests
Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods, are unsatisfying. We discuss and evalu…
Generalization BoundsLearning to Benchmark: Determining Best Achievable Misclassification Error from Training Data
We address the problem of learning to benchmark the best achievable classifier performance. In this problem the objective is to establish statistically consistent estimates of the Bayes misclassification error rate witho…