Data-Dependent Structural Risk Minimization for Perceptron Decision Trees
نویسندگان
چکیده
Perceptron Decision Trees also known as Linear Machine DTs etc are analysed in order that data dependent Structural Risk Minimization can be applied Data dependent analysis is performed which indicates that choosing the maximal margin hyperplanes at the decision nodes will im prove the generalization The analysis uses a novel technique to bound the generalization error in terms of the margins at individual nodes Experi ments performed on real data sets con rm the validity of the approach
منابع مشابه
Data-dependent Structural Risk Minimisation for Perceptron Decision Trees Produced as Part of the Esprit Working Group in Neural and Computational Learning Ii, Neurocolt2 27150
Perceptron Decision Trees (also known as Linear Machine DTs, etc.) are analysed in order that data-dependent Structural Risk Minimization can be applied. Data-dependent analysis is performed which indicates that choosing the maximal margin hyperplanes at the decision nodes will improve the generalization. The analysis uses a novel technique to bound the generalization error in terms of the marg...
متن کاملModel selection in omnivariate decision trees using Structural Risk Minimization
As opposed to trees that use a single type of decision node, an omnivariate decision tree contains nodes of different types. We propose to use Structural Risk Minimization (SRM) to choose between node types in omnivariate decision tree construction to match the complexity of a node to the complexity of the data reaching that node. In order to apply SRM for model selection, one needs the VC-dime...
متن کاملMinimizing Structural Risk on Decision Tree Classification
Tree induction algorithms use heuristic information to obtain decision tree classification. However, there has been little research on how many rules are appropriate for a given set of data, that is, how we can find the best structure leading to desirable generalization performance. In this chapter, an evolutionary multi-objective optimization approach with genetic programming will be applied t...
متن کاملDyadic Classification Trees via Structural Risk Minimization
Classification trees are one of the most popular types of classifiers, with ease of implementation and interpretation being among their attractive features. Despite the widespread use of classification trees, theoretical analysis of their performance is scarce. In this paper, we show that a new family of classification trees, called dyadic classification trees (DCTs), are near optimal (in a min...
متن کاملOracle Inequalities and Adaptive Rates
We have previously seen how sieve estimators give rise to rates of convergence to the Bayes risk by performing empirical risk minimization over Hk(n), where (Hk)k ≥ 1 is an increasing sequence of sets of classifiers, and k(n) → ∞. However, the rate of convergence depends on k(n). Usually this rate is chosen to minimize the worst-case rate over all distributions of interest. However, it would be...
متن کامل