This paper reports the implementation of DRAPH-GP an extension of the decision graph algorithm DGRAPH-OW using the AdaBoost algorithm. This algorithm, which we call 1Stage Boosting, is shown to improve the accuracy of decision graphs, along with another technique which we combine with AdaBoost and call 2-Stage Boosting which shows greater improvement. Empirical tests demonstrate that both 1-Sta...

We present an extended abstract about boosting. We describe first in section 1 (in a self-contained way) a generic functional gradient descent algorithm, which yields a general representation of boosting. Properties of boosting or functional gradient descent are then very briefly summarized in section 2.

Although boosting methods have become an extremely important classification method, there has been little attention paid to boosting with asymmetric losses. In this paper we take a gradient descent view of boosting in order to motivate a new boosting variant called BiBoost which treats the two classes differently. This variant is likely to perform well when there is a different cost for false p...

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 ...

Gradient tree boosting is a prediction algorithm that sequentially produces a model in the form of linear combinations of decision trees, by solving an infinite-dimensional optimization problem. We combine gradient boosting and Nesterov’s accelerated descent to design a new algorithm, which we call AGB (for Accelerated Gradient Boosting). Substantial numerical evidence is provided on both synth...

Real-time object detection is one of the core problems in computer vision. The cascade boosting framework proposed by Viola and Jones has become the standard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate false positive rate. We develop new boosting algorithms to address this asymmetric learni...

In this paper we present an empirical comparison of algorithm AdaBoost with its modification called MadaBoost suitable for the boosting by filtering framework. In the boosting by filtering one obtains an unweighted sample at each stage that is randomly drawn from the current modified distribution in contrast with the boosting by subsampling where one uses a weighted sample at each stage. A boos...

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...

Boosting is a well known and efficient technique for constructing a classifier ensemble. An ensemble is built incrementally by altering the distribution of training data set and forcing learners to focus on misclassification errors. In this paper, an improvement to Boosting algorithm called DivBoosting algorithm is proposed and studied. Experiments on several data sets are conducted on both Boo...