My decision to pursue a PhD is derived from my passion for science and engineering paired with my abilities in the field of machine learning and applied statistics. I consider myself fortunate to be part of the Department of Computer Science, University of Florida for my master studies. More importantly, I am glad to have two excellent professors in this field as advisors, Dr. Pader and Dr. Jilson, who are guiding me throughout my graduate studies. They assisted me to decide and pursue the courses and topics that interested me. During my first semester, I took the course Mathematical methods for Intelligent Systems that gave me a strong base for applied mathematics in the field of intelligent systems. Similarly, the research course Computational Neuroscience gave me an insight into applications of statistics, neural networks, and linear dynamical systems in a biological perspective. My keen interest towards the field of applied statistics, inspired me to take courses such as Machine learning and Neural Networks in the subsequent semester. In this context, I would like to give a brief outline of my master’s research projects, which are I found to be very exciting. The first project was to design a Handwritten Recognition system capable of classifying the digits using the Multilayer Perceptron architecture. Another project was a comparative study of machine learning methodologies such as Bayesian Linear Regression (BLR), Support Vector Machines (SVMs), and Relevance Vector Machines (RVMs), using handwritten character data from postal system. In the first phase, we analyzed the capability of mapping the features calculated on the input character images to membership values in different classes using BLR. In the second phase, the c... ... middle of paper ... ...ilar queries that belong to a particular domain. Representing user queries in a machine-readable format will help us building probabilistic models for queries. Moreover, combining queries to solve complex queries will be next milestone in question answering systems. Works Cited 1. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet Allocation, JMLR, 2003 2. C. Bizer, J. Lehmann, G. Kobilarov, S. Auer, C. Becker, R. Cyganiak, and S. Hellmann. Dbpedia – a crystallization point for the web of data. Web Semantics: Science, Services and Agents on the WWW, September 2009. 3. F. M. Suchanek. Automated Construction and Growth of a Large Ontology. PhD thesis, Saarland University, 2009. 4. T. Hofmann, SIGIR '99: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval (ACM, New York, NY, USA, 1999)
A paper by Chang et al.2009 conducts user studies to quantitatively compare the semantic meaning in topics inferred by PLSA and LDA. The focus is to quantify the interpretability of topics with human effort, The author of this paper(current) study the task performance of topic models in three standard text mining applications, which can be quantified objectively using standard measures. So this work is supplementary to theirs.
“Artificial Intelligence.” Issues & Controversies. Facts On File News Services. 11 July 2011. Web. 23 March 2014.
Waltz, David L. “Artificial Intelligence: Realizing the Ultimate Promises of Computing.” NEC Research Institute and the Computing Research Association (1996): 2 November 1999 .
There will be difference in the level of details given by different ontologies on the same domain. This poses extra challenges to select the ontology that has the accurate level of detail. Ontology selection is the process of identifying one or more ontologies that satisfy certain criteria. These criteria can be related to topic coverage of the ontology. The actual process of inspecting whether ontology satisfies certain criteria is fundamentally an ontology evaluation task. In this approach ontology concepts are compared to a set of query terms that represent the domain. It first tries to determine ontologies that contain the given keyword. If no matches are found, it queries for the synonyms of the term and then for its hypernyms. The ontology se...
The World Wide Web is full of information that can improve and enrich our lives. In order to benefit from all of this information it must exhibit some type of relevance to our everyday lives and one must also be able to read and interpret this information so that it is useful to us.
Artificial Intelligence (AI) is one of the newest fields in Science and Engineering. Work started in earnest soon after World War II, and the name itself was coined in 1956 by John McCarthy. Artificial Intelligence is an art of creating machines that perform functions that require intelligence when performed by people [Kurzweil, 1990]. It encompasses a huge variety of subfields, ranging from general (learning and perception) to the specific, such as playing chess, proving mathematical theorems, writing poetry, driving a car on the crowded street, and diagnosing diseases. Artificial Intelligence is relevant to any intellectual task; it is truly a Universal field. In future, intelligent machines will replace or enhance human’s capabilities in
As the conclusion, the paper made good contribution to the field by describing the history of the information retrieval systems from 1945 to 1996 with abundant information on the various technologies developed, information retrieval systems built, and how they affected the research in information retrieval. I think artificial intelligence will start to play a leading role in information retrieval in the following years and one day we will have true question answering type of information retrieval at the finger tip of every Internet user.
As I watched my mother rush to get the pot to boil some water with tears in
Information Retrieval is simply a field concerned with organizing information. In other terms, IR is emphasizing the range of different materials that need to be searched. Others researcher said that IR is the contrast between the strong structure and typing a database system with the lack of structure in the objects typically searched in IR. The actual process in information retrieval systems is it has to deal with incomplete or under specified information in the form of the queries issued by users. IR uses the techniques of storing and recovering and often disseminating recorded data especially through the use of a computerized system.
Artificial Intelligence may come in many forms, but for the purpose of this paper, I have adopted the definition from The Columbia Encyclopedia (2008), which states that Artificial Intelligence (AI) is a discipline of computer science that aims to focus on the creation of machines that can mimic intelligent human behavior. It is the attempt to give computers human reasoning and thought processes. Humans have always had an interest in the design, creation and application of smart machines. Consequently, with the discovery and introduction of computer systems and with the decades of research in programming that has followed, humans have now realized that many of their ideas may be possible by the development of these systems. The most intriguing issue with this field of study is that as time passes, technology changes and so does the definition of Artificial Intelligence. It is, in a lighter definition of intelligence, the distinct applications of unnaturally occurring systems or application of artificial systems that rely on use of different knowledge levels to achieve set goals. Artificial Intelligence began with the theoretical work of mathematician Alan T...
The mind must never be kept idle because of its tremendous capacity to absorb and learn. After careful consideration of my aptitude, interests and experiences gained while pursuing my under graduation in the field of Computer Science and Engineering, I have decided to pursue my Masters in the field of Computer Science. Being a dynamic and ever evolving field, many new developments are expected and there is immense scope for research on new products and applications. To progress and make a mark in this field, I realize that it is important for me to pursue my Masters from a reputable university. I have always dreamt of taking up research.
T. Mitchell, Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Draft Version, 2005 download
Artificial Intelligence is the scientific theory to advance the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines. This is going to hold the key in the future. It has always fa...
Lesk, M. (1995). The seven ages of information retrieval. Retrieved October 26, 2011 from http://archive.ifla.org/VI/5/op/udtop5/udtop5.htm#10
Summary- This book expert describes the fundamentals, history, and changes associated with Artificial Intelligence from 1950’s onward. The book provides a basic explanation that Artificial Intelligence involves simulating human behavior or performance using encoded thought processes and reasoning with electronic free standing components that do mechanical work.