SURF Fellowship @ California Institute of Technology
Summer Research Intern @ George Mason University
Thomas Jefferson High School for Science and Technology
Software Engineering Intern @ From June 2014 to September 2014 (4 months) SURF Fellowship @ An essential function of computer vision algorithms is to perform object classification within images. A specific problem within the field of object classification is bird species classification, a task which is difficult even for humans. The goal is not to just develop
Once this time profile or time signature of an object is generated, a similar analysis can be run on another object a in different image and see if the two time signatures are related. The goal of the project is to first perform the time signature analysis on an object and then find a way to compare the two time signatures that are produced. An algorithm to run PCNNs on an image and generate a time signature for an object was created, written in python. Unfortunately, there was not enough time to find a mathematical way to compare the time signatures of two different objects. From June 2012 to August 2012 (3 months) Summer Research Intern @ Neural networks are a well-known machine learning technique. Machine learning through neural networks is the process of teaching a network to recognize a set of training vectors and training outputs by producing the correct output for each vector. This produces generalized behavior, so the network can recognize similar vectors that it has never been shown before. This project deals with the case where a conflict exists in the training vectors. A conflict necessitates a higher order network with another neuron, a hidden neuron, added to resolve the conflict. The goal is to identify these conflicts to gain more information about the system, rather than simply resolving them. The algorithm used to find these conflicts involved a removal of training vectors and components of these vectors through an iterative process. The advantage of finding conflicts rather than only resolving them is a greater knowledge about the system and the complexity inherent in the training data.
Though machine learning has manifold applications, an application to text mining in particular was considered. Given two topics and a set of documents related to each topic, the goal was to find combinations of words, rather than just single keywords, that related two or more different documents. By pruning the documents and giving each word a ranking based on the tf-idf (term frequency, inverse document frequency) algorithm, a set of vectors was created. The vectors and components of vectors that participate in a conflict would then correspond to the combinations of words that uniquely identify multiple documents. From June 2011 to August 2011 (3 months)
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