A two-step procedure for nonparametric rnulticlass classifier design is described. A multiclass recursive partitioning algorithm is given which generates a single binary decision tree for classifying all classes. The algorithm minimizes the Bayes risk at each node. A tree termination algorithm is given which optimally terminates binary decision trees. The algorithm yields the unique tree with fewest nodes which minimizes the Bayes risk. Tree generation and termination are based on the training and test samples, respectively. We state the nonparametric multiclass classification problem as follows. M classes are characterized by unknown probability distribution functions. A data samrple containing labelled vectors from each of the It classes ...
The alternating decision tree (ADTree) is a successful classification technique that combine decisio...
MasterClassification is to generate a set of rule of classifying objects into severalcategories base...
Classifiers can be either linear means Naive Bayes classifier or non-linear means decision trees.In ...
Bibliography: p. 15."October 1984.""...supported by the U.S. Army Research Office under Grant DAAG29...
c classes are characterized by unknown probability distributions. A data sample containing labelled ...
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
4 M NITRIN AE C 1 ~ AM &AOOESSIIdifferent from, Controlling Office) IS. SECURITY CLASS. (of tlIe...
1 Introduction Decision tree algorithms (e.g., [14, 3]) have to solve two distinct problems: they ne...
We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. ...
Various popular machine learning techniques, like support vector machines, are originally conceived ...
The alternating decision tree (ADTree) is a successful classification technique that combines decisi...
An algorithm for learning decision trees for classification and prediction is described which conver...
The alternating decision tree (ADTree) is a successful classification technique that combines decis...
One-class classifiers are trained only with target class samples. Intuitively, their conservative mo...
Several real problems involve the classification of data into categories or classes. Given a data se...
The alternating decision tree (ADTree) is a successful classification technique that combine decisio...
MasterClassification is to generate a set of rule of classifying objects into severalcategories base...
Classifiers can be either linear means Naive Bayes classifier or non-linear means decision trees.In ...
Bibliography: p. 15."October 1984.""...supported by the U.S. Army Research Office under Grant DAAG29...
c classes are characterized by unknown probability distributions. A data sample containing labelled ...
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer S...
4 M NITRIN AE C 1 ~ AM &AOOESSIIdifferent from, Controlling Office) IS. SECURITY CLASS. (of tlIe...
1 Introduction Decision tree algorithms (e.g., [14, 3]) have to solve two distinct problems: they ne...
We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. ...
Various popular machine learning techniques, like support vector machines, are originally conceived ...
The alternating decision tree (ADTree) is a successful classification technique that combines decisi...
An algorithm for learning decision trees for classification and prediction is described which conver...
The alternating decision tree (ADTree) is a successful classification technique that combines decis...
One-class classifiers are trained only with target class samples. Intuitively, their conservative mo...
Several real problems involve the classification of data into categories or classes. Given a data se...
The alternating decision tree (ADTree) is a successful classification technique that combine decisio...
MasterClassification is to generate a set of rule of classifying objects into severalcategories base...
Classifiers can be either linear means Naive Bayes classifier or non-linear means decision trees.In ...
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