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)
This paper deals with the task performance of PLSA(Probabilistic Latent Semantic Analysis) and LDA(Latent Dirichlet Allocation). There has been lot of work done, reporting promising performance of topic models, but none of the work has systematically investigated the task performance of topic models. As a result, some critical questions that may affect the performance of all applications of topic models are mostly unanswered, particularly
Waltz, David L. “Artificial Intelligence: Realizing the Ultimate Promises of Computing.” NEC Research Institute and the Computing Research Association (1996): 2 November 1999 .
Goertzel, B., & Pennachin, C. (2007). In Artificial General Intelligence. Heidelburg, New York: Springer Berlin. Retrieved on July 31, 2010 from Google books Database.
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.
Please discuss the following items in the order given. Briefly respond to all areas listed.
The first IR system was built which used indexes and concordances. When the first large scale information systems were developed, computers can search indexes must better than human, which required more detailed indexing. However, indexing could also become too expensive and time consuming. Therefore, the idea of free-text searching is initiated, which eliminates the need for manual indexing. Objections pointed out that selecting the right words might not be the correct label for a given subject. One solution is official vocabularies. The idea of recall and precision also came out as methods for evaluating information retrieval systems, and they showed that free-text indexing was as effective as manual indexing and much cheaper. New information retrieval techniques such as relevance feedback, multi-lingual retrieval were invented. The 1960s also was the start of research into natural language question-answering, and researchers began building systems ...
As I watched my mother rush to get the pot to boil some water with tears in
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.
In today’s fast paced technology, search engines have become vastly popular use for people’s daily routines. A search engine is an information retrieval system that allows someone to search the...
...ast predictions of how fast AI will progress turned out to be misleading. This does not however mean that it has completely failed. Rather, it means that there was mere misunderstanding of the problems in those predictions involving the sciences and engineering. The future advancement of AI will require only the brightest of minds from many fields that include sciences, engineering, neurosciences, linguistics, philosophy, psychology, and most importantly, mathematics. Progress will be achieved as long as humans keep their imaginations and desire to achieve goals because with AI it is not only difficult, but also exciting (Sloman, 2009). In the 21st Century, Artificial Intelligence research will aim to add reasoning and knowledge to its existing applications, which makes it smarter, easier to use, more flexible, and increase its sensitivity to environmental changes
IR systems receiving such queries need to fill in the gaps of the user’s underspecified query. For example, user typing “nuclear waste dumpling” into the search engine such as Google is probably searching for multiple number of documents that describing the topic. Some of the documents might not archive what user need as the search engine search documents that relate to the three worlds only. The content being searched is typically unstruc...
It is that passion which has grown over the years into a single-minded pursuit of Computer Science as a serious academic career, and led me to pursue a B.Tech in Information Technology at Delhi Technological University (DTU; formerly Delhi College of Engineering), one of the premier institutions in the country. I aspire to attain a doctorate in the areas of Artificial Intelligence (AI) and Natural Language Processing (NLP). I believe Berkeley’s MS in Computer Science will help me expand both the breadth and depth of my knowledge in these areas and allow me to identify a specialization for a subsequent doctoral degree.
First part that relates to the information retrieval with the life span of person is the challenges part or the tension part between the simple statistical method and sophisticated information analysis. At this part, the translation problem is being highlighted. This is a common problem in cross-lingual information system (Bounsaythip, Lehtola & Tenni) where when using a query expressed in the second language, the most relevant documents in the translated subset are extracted (usually using a cosine measure of proximity). These relevant documents are in turn used to extract close untranslated documents in the subspace of the first language. (Fluhr, 1996). The ideas of translating language is also being highlighted in this article when the author’s code the Warren Weaver memo in 1949. Weaver used to study on machine translation while...
Ontology contains a set of concepts and relationship between concepts, and can be applied into information retrieval to deal with user queries.
T. Mitchell, Generative and Discriminative Classifiers: Naive Bayes and Logistic Regression. Draft Version, 2005 download