Video retrieval compares multimedia queries to a video collection in multiple dimensions and combines all the retrieval scores into a nal ranking. Although text are the most reliable feature for video retrieval, features from other modalities can provide complementary information. This paper presents a reranking framework for video retrieval to augment retrieval based on text features with othe...

We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boosting algorithms are closely related to models of mislabeling in which the label is erroneously exchanged for others. For the two boosting algorithms, theoretical aspects supportin...

In this paper, we investigate the theoretical and empirical properties of L2 boosting with kernel regression estimates as weak learners. We show that each step of L2 boosting reduces the bias of the estimate by two orders of magnitude, while it does not deteriorate the order of the variance. We illustrate the theoretical findings by some simulated examples. Also, we demonstrate that L2 boosting...

Gradient Boosting and bagging applied to regressors can reduce the error due to bias and variance respectively. Alternatively, Stochastic Gradient Boosting (SGB) and Iterated Bagging (IB) attempt to simultaneously reduce the contribution of both bias and variance to error. We provide an extensive empirical analysis of these methods, along with two alternate bias-variance reduction approaches — ...

The AdaBoost like algorithm for boosting CART regression trees is considered. The boosting predictors sequence is analyzed on various data sets and the behaviour of the algorithm is investigated. An instability index of a given estimation method with respect to some training sample is defined. Based on the bagging algorithm, this instability index is then extended to quantify the additional ins...

Boosting is one of the most significant development in machine learning areas in recent years. Although boosting has already achieved great success in practical applications, its internal mechanism has not been entirely understood. In this paper, we present a new perspective to design boosting algorithms: extracting independent weak rules. A boosting algorithm can be divided into two parts, an ...

Classiier committee learning approaches have demonstrated great success in increasing the prediction accuracy of classiier learning , which is a key technique for datamining. These approaches generate several classiiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the nal classiication. It has been shown that Boosting and ...