”Boosting” is a general method for improving the performance of almost any learning algorithm. A recently proposed and very promising boosting algorithm is AdaBoost [7]. In this paper we investigate if AdaBoost can be used to improve a hybrid HMM/ neural network continuous speech recognizer. Boosting significantly improves the word error rate from 6.3% to 5.3% on a test set of the OGI Numbers95...

We saw last time that the training error of AdaBoost decreases exponentially as the number of rounds T grows. However, this says nothing about how well the function output by AdaBoost performs on new examples. Today we will discuss the generalization error of AdaBoost. We know that AdaBoost gives us a consistent function quickly; the bound we derived on training error decreases exponentially, a...

AdaBoost !5] is a well-known ensemble learning algorithm that constructs its constituent or base models in sequence. A key step ill AdaBoost is constructing a distribution over the training examples to crette each base model. This distribution, represented as a vector, is constructed to be orthogonal to the vector of mistakes made by tLe previous base model in the sequence [6]. The idea is to m...

The structural class is an important feature in characterizing the overall topological folding type of a protein or the domains therein. Prediction of protein structural classification has attracted the attention and efforts from many investigators. In this paper a novel predictor, the AdaBoost Learner, was introduced to deal with this problem. The essence of the AdaBoost Learner is that a comb...

The Adaptive Boosting (AdaBoost) classifier is a widely used ensemble learning framework, and it can get good classification results on general datasets. However, challenging to apply the AdaBoost directly pulmonary nodule detection of labeled unlabeled lung CT images since there are still some drawbacks method. Therefore, solve data problem, semi-supervised using an improved sparrow search alg...

Submission to NIPS*97, Category: Algorithms & Architectures, Preferred: Oral ”Boosting” is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several ben...

”Boosting” is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [4],...

"Boosting" is a general method for improving the performance of any learning algorithm that consistently generates classifiers which need to perform only slightly better than random guessing. A recently proposed and very promising boosting algorithm is AdaBoost [5]. It has been applied with great success to several benchmark machine learning problems using rather simple learning algorithms [4],...