Current systems for face recognition techniques often use either SVM or Adaboost techniques for face detection part and use PCA for face recognition part. In this paper, we offer a novel method for not only a powerful face detection system based on Six-segment-filters (SSR) and Adaboost learning algorithms but also for a face recognition system. A new exclusive face detection algorithm has been...

In human body genetic codes are stored in the genes. All of our inherited traits associated with these genes and grouped as structures generally called chromosomes. typical cases, each cell consists 23 pairs chromosomes, out which parent contributes half. But if a person has partial or full copy chromosome 21, situation is Down syndrome. It results intellectual disability, reading impairment, d...

Methods for voting classiication algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classiiers for artiicial and real-world datasets. We review these algorithms and describe a large empirical study comparing several variants in conjunction with a decision tree inducer (three variants) and a Naive-Bayes inducer. The purpose of the...

Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. The most influential work is the margin theory, which is essentially an upper bound for the generalization error of any voting classifier in terms of the margin distribution over the training data. However, Breiman raised important questions about the margin explanation by developing a boosting alg...

We investigate improvements of AdaBoost that can exploit the fact that the weak hypotheses are one-sided, i.e. either all its positive (or negative) predictions are correct. In particular, for any set of m labeled examples consistent with a disjunction of k literals (which are one-sided in this case), AdaBoost constructs a consistent hypothesis by using O(k logm) iterations. On the other hand, ...

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater in...

Semantic concept classification is a critical task for content-based video retrieval. Traditional methods of machine learning focus on increasing the accuracy of classifiers or models, and face the problems of inducing new data errors and algorithm complexity. Recent researches show that fusion strategies of ensemble learning have appeared promising for improving the classification performance,...

This paper explores how multi-armed bandits (MABs) can be applied to accelerate AdaBoost. AdaBoost constructs a strong classifier in a stepwise fashion by adding simple base classifiers to a pool and using their weighted “vote” to determine the final classification. We model this stepwise base classifier selection as a sequential decision problem, and optimize it with MABs. Each arm represents ...

Viola and Jones [1] introduced a new and effective face detection algorithm based on simple features trained by the AdaBoost Algorithm, Integral Images and Cascaded Feature sets. This paper attempts to replicate their results. The Feret Face data set is used as the training set. The AdaBoost Algorithm, simple feature set and Integral Images are briefly explained and implemented in our Matlab ba...