Cost-Aware Bayesian Sequential Decision-Making for Search and Classification
نویسندگان
چکیده
منابع مشابه
Fusion of Modular Bayesian Networks for Context-Aware Decision Making
Ubiquitous computing brings various information and knowledge derived from different sources, under which Bayesian networks are widely used to cope with the uncertainty and imprecision. In this paper, we propose a modular Bayesian network system to extract context information by cooperative inference of multiple modules, which guarantees reliable inference compared to the monolithic Bayesian ne...
متن کاملPredicting Twist Condition by Bayesian Classification and Decision Tree Techniques
Railway infrastructures are among the most important national assets of countries. Most of the annual budget of infrastructure managers are spent on repairing, improving and maintaining railways. The best repair method should consider all economic and technical aspects of the problem. In recent years, data analysis of maintenance records has contributed significantly for minimizing the costs. B...
متن کاملConvergence in a sequential two stages decision making process
We analyze a sequential decision making process, in which at each stepthe decision is made in two stages. In the rst stage a partially optimalaction is chosen, which allows the decision maker to learn how to improveit under the new environment. We show how inertia (cost of changing)may lead the process to converge to a routine where no further changesare made. We illustrate our scheme with some...
متن کاملSequential Bayesian Search
Millions of people search daily for movies, music, and books on the Internet. Unfortunately, non-personalized exploration of items can result in an infeasible number of costly interaction steps. We study the problem of efficient, repeated interactive search. In this problem, the user is navigated to the items of interest through a series of options and our objective is to learn a better search ...
متن کاملSequential Bayesian Search Appendices
Assume that at the beginning of game t, the system's belief in the user's preference is P t. Then, the certainty-equivalet user preference during game t is π * t (i) = E π∼Pt [π(i)] ∀i ∈ I. Recall we define π * min = min i∈I π * (i), Lemma A-1 formalizes the result that if π * t is " close " to π * , then for any decision tree T , E i∼π * t [N (T, i)] is " close " to E i∼π * [N (T, i)]: Lemma A...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Aerospace and Electronic Systems
سال: 2012
ISSN: 0018-9251
DOI: 10.1109/taes.2012.6237609