AdaBoost is among one of the most extensively studied and used algorithms, known widely with its shorter name. Short for Adaptive Boosting, the algorithms deals with the machine learning mechanisms. Instead of involving single inaccurate procedures like the rule of thumb, which are little more than guess work, AdaBoost maintains concrete machine learning using a combination of various rules that yield inaccurate and weak results.
Consequently, the AdaBoost algorithm becomes the most accurate prediction rule. It was designed by the combined efforts of Robert Schapire and Yoav Freund, who have been appreciated and given awards for their amazing work. Since it is such a ground breaking innovation, extensive amount of literature has been produced on it. Robert E. Schapire, one of the creators of the algorithm and a professor at Princeton University explains the perspectives of AdaBoost. In his paper, he discusses how it is a learning system, and makes use of the concept of eliminating the odd one out. It does not make use of a new, highly evolved technique. Instead, it takes the seasoned and old techniques which are highly inaccurate, and makes use of them to produce precise results. This is achieved in a very smart process, where the inaccurate process are applied multiple times such that outlier values are eliminated, leaving the highly accurate and correct solutions [40]. In his paper submitted to MIT (Massachusetts institute of Technology), Tommi Jaakkola writes about the practical advantages of using AdaBoost. He mentions that it is a very fast system, which happens to be rather easy to use as well, owing to its simplicity [41].
be used with a combination of programs. Hence it is a highly versatile program, which can serve multiple purposes. Even the data that it operates with needs not be restricted as it can comprehend textual, discrete, numeric, binary and more or less any other type of data set. From programming to predictions and detection of similarities, the uses of AdaBoost are limitless. In their research paper, students of Colombia University presented a list of applications among which AdaBoost can be distributed. Their proposal was to use this mechanism for classification of datasets that vary highly and are distributed such that fitting them into memory is a nearly impossible task. The study proposes the use of this algorithm such that it helps in on-line, scalable and distributed learning [42]. This will enable it to be used in application systems like JAVA and JAM. There are other places where this system can be of critical importance and extreme help. Research has been conducted to put things into perspective and try to make use of this algorithm in basketball games to identify players [43]. After conducting a series of tests on simple moving objects instead of the entire games with the same backgrounds, it was found out that the algorithm is sensitive to the noise in the background. This noise is the reason which ended up distorting the results to such a degree that the detection of players was found out to be accurate only 70.5% of the times. The accuracy may seem impressive when compared to other methods, but the fact remains that it is not nearly close to the amount required for recognition of players in actual games. Where AdaBoost will be rendered useless in recognizing the players through the complete body parts, the same program is found out to be very effective in recognizing the body parts on their own. This indicates that the algorithm holds potential in recognition of singular parts, like facial recognition. It may be inaccurate in real situations, particularly in sports related events, but its fast pace of scrutinizing through the immense quantity of data and ability to recognize features as long as they do not involve multiple aspects, make it a very suitable system for object detection.
In addition to the detection of objects, the algorithm works perfectly for image restoration, that as well without any prior knowledge at hand [44]. Through this method, both the distorted and degraded images can be perfectly restored. According
to a paper submitted to the University of Los Angeles, by X. Chen and A.L. Yuille AdaBoost can be very useful in detecting and reading texts within the scenes of a city. This has lead to a very useful application go the algorithm, which had provided ground breaking assistance to the people who are visually impaired. Not only has the algorithm given very speedy responses (as fast as processing of images of size 2048 by 1536 within 9 seconds), but at the same time it has also given high quality results which are accurate up to 93% [45]. But of all its applications, perhaps the most significant one is related to the assistance that AdaBoost has provided to the systems of face detection [46]. Using an array of complex design techniques like those of classification, image scaling, pipelined processing and integral image generation not only is the process of face detection possible, but is also accelerated to an incredible speed. This process makes use of Integral images and Haar features, while ignoring things like size, condition and position of the face.
The ultimate product is used for many purposes in different places in the contem- porary era. It is used for security purposes in computer interfaces and smart rooms, for granting access to some specific people, while denying it to others. The intelligent robots of today also make use of this technology to integrate and work with humans in controlled environments. It has rendered its use in image analysis of biomedical degree. From surveillance cameras to tracking criminals through CCTV footage, the incredible amount of security related advantages that we claim and make us of today are a product of AdaBoost algorithm.