The face pattern is described by extracted features using the new Reduced Joint Integral Histogram (RJIH) data structure. Extending the classical representations of integral images and integral histograms, it joins the global information of two images. Then, we turn to Linear Discriminant Analysis (LDA) to project the obtained Joint Integral Histogram from d−dimensional subspace to one dimensio...

In this paper, a novel face recognition method based on Real AdaBoost algorithm and Kalman Forecast is implemented. Real AdaBoost algorithm can obtain great accuracy with machine learning. Meanwhile, Kalman Forecast is introduced to track human faces detected, making face detection more efficient. We tested our new method with many video sequences. The detection accuracy is 98. 57%, and the ave...

In this paper we examine master regression algorithms that leverage base regressors by iteratively calling them on modified samples. The most successful leveraging algorithm for classification is AdaBoost, an algorithm that requires only modest assumptions on the base learning method for its good theoretical bounds. We present three gradient descent leveraging algorithms for regression and prov...

The face pattern is described by pairs of template-based histogram and Fisher projection orientation under the paradigm of AdaBoost learning in this paper. We assume that a set of templates are available first. To avoid making strong assumptions about distributional structure while still retaining good properties for estimation, the classical statistical model, histogram, is used to summarize t...

AdaBoost is a popular and eeective leveraging procedure for improving the hypotheses generated by weak learning algorithms. AdaBoost and many other leveraging algorithms can be viewed as performing a constrained gradient descent over a potential function. At each iteration the distribution over the sample given to the weak learner is the direction of steepest descent. We introduce a new leverag...

Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a weighted vote of their predictions The original ensemble method is Bayesian aver aging but more recent algorithms include error correcting output coding Bagging and boosting This paper reviews these methods and explains why ensembles can often perform better than any single ...

Boosting approaches are based on the idea that high-quality learning algorithms can be formed by repeated use of a “weak-learner”, which is required to perform only slightly better than random guessing. It is known that Boosting can lead to drastic improvements compared to the individual weak-learner. For two-class problems it has been shown that the original Boosting algorithm, called AdaBoost...

a r t i c l e i n f o The idea of boosting deeply roots in our daily life practice, which constructs the general aspects of how to think about chemical problems and how to build chemical models. In mathematics, boosting is an iterative reweighting procedure by sequentially applying a base learner to reweighted versions of the training data whose current weights are modified based on how accurat...