M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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Has files: YesSubject: search.filters.subject.Bayesian statistical decision theory

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    Learning to rank: using Bayesian networks
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Gupta, Parth; Mjumder, Prasenjit; Mitra, Suman K.
    Ranking is one of the key components of an Information Retrieval system. Recently supervised learning is involved for learning the ranking function and is called 'Learning to Rank' collectively. In this study we present one approach to solve this problem. We intend to test this problem in di erent stochastic environment and hence we choose to use Bayesian Networks for machine learning. This work also involves experimentation results on standard learning to rank dataset `Letor4.0'[6]. We call our approach as BayesNetRank. We compare the performance of BayesNetRank with another Support Vector Machine(SVM) based approach called RankSVM [5]. Performance analysis is also involved in the study to identify for which kind of queries, proposed system gives results on either extremes. Evaluation results are shown using two rank based evaluation metrics, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG).
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    Use of collaborative filtering for targeted advertising
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2008) Upadhyay, Ankur; Jotwani, Naresh D.
    Often in our daily life, we come across situations when we have many options available and are expected to choose one of them. May it be a bookstore, CD shop or a shopping store, even on the internet, the availability of so many genres and a wide variety among every genre poses difficulty in selection of the item. Recommender systems have been providing suggestions but they are not able to provide us options when we are walking through the aisles of a bookstore or a CD shop. Ideally, recommendations should be made available to the customer without giving explicit command. To provide ease while walking down for shopping in selecting the items based on the item chosen by the customer, the topic focuses on deriving a general model for recommending a product that might save customers money and time along with fulfilling the need. Selection of the product to be advertised by the model is a dynamic decision as it depends on the products kept in the basket. Bayesian approach is used to find the dependencies between items which implements Collaborative filtering and provides real time recommendations on the basis of preferences of earlier customers. The model uses Clustering to limit the complexity of the model that will be built and to aggregate similar items, by grouping customers those who bought items of similar genre. The assumption made is that the selection of the customer is made known to the model in order to process it, to give recommendations; and the recommendations are made known to customers using suitable advertising mechanism.
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    Study of bayesian learning of system characteristics
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2008) Sharma, Abhishek; Jotwani, Naresh D.
    This thesis report basically deals with the scheduling algorithms implemented in our computer systems and about the creation of probabilistic network which predicts the behavior of system. The aim of this thesis is to provide a better and optimized results for any system where scheduling can be done. The material presented in this report will provide an overview of the field and pave the way to studying subsequent topics which gives the detailed theories on Bayesian networks, learning the Bayesian networks and the concepts related to the process scheduling. Bayesian network is graphical model for probabilistic relationships among a set of random variables (either discrete or continuous). These models having several advantages over data analysis. The goal of learning is to find the Bayesian network that best represents the joint probability distribution. One approach is to find the network that maximizes the likelihood of the data or (more conveniently) its logarithm. We describe the methods for learning both the parameters and structure of a Bayesian network, including techniques for learning with complete data also. We relate Bayesian network methods for learning, to learn from data samples generated from the operating system scheduling environment. The various results produced, tested and verified for scheduling algorithms (FCFS, SJF, RR and PW) by an Operating System Scheduling Simulator implemented in programming language JAVA. Here, the given code is modified according to requirements and fulfilling the necessary task.