Browsing by Subject "Inference engines"

Now showing 1 - 5 of 5

Open Access
Computation of the resonant frequency of electrically thin and thick rectangular microstrip antennas with the use of fuzzy inference systems
(John Wiley & Sons, 2000) Özer, Ş.; Güney, K.; Kaplan, A.
A new method for calculating the resonant frequency of electrically thin and thick rectangular microstrip antennas, based on the fuzzy inference systems, is presented. The optimum design parameters of the fuzzy inference systems are determined by using the classical, modified, and improved tabu search algorithms. The calculated resonant frequency results are in very good agreement with the experimental results reported elsewhere.
Open Access
Information-based approach to punctuation
(AAAI, 1997-07) Say, Bilge
This thesis analyzes, in an information-based framework, the semantic and discourse aspects of punctuation, drawing computational implications for Natural Language Processing (NLP) systems. The Discourse Representation Theory (DRT) is taken as the theoretical framework of the thesis. By following this analysis, it is hoped that NLP software writers will be able to make use of the punctuation marks effectively as well as reveal interesting linguistic phenomena in conjunction with punctuation marks.
Open Access
Location recommendations for new businesses using check-in data
(IEEE, 2016-12) Eravci, Bahaeddin; Bulut, Neslihan; Etemoğlu, C.; Ferhatosmanoğlu, Hakan
Location based social networks (LBSN) and mobile applications generate data useful for location oriented business decisions. Companies can get insights about mobility patterns of potential customers and their daily habits on shopping, dining, etc.To enhance customer satisfaction and increase profitability. We introduce a new problem of identifying neighborhoods with a potential of success in a line of business. After partitioning the city into neighborhoods, based on geographical and social distances, we use the similarities of the neighborhoods to identify specific neighborhoods as candidates for investment for a new business opportunity. We present two solutions for this new problem: i) a probabilistic approach based on Bayesian inference for location selection along with a voting based approximation, and ii) an adaptation of collaborative filtering using the similarity of neighborhoods based on co-existence of related venues and check-in patterns. We use Foursquare user check-in and venue location data to evaluate the performance of the proposed approach. Our experiments show promising results for identifying new opportunities and supporting business decisions using increasingly available check-in data sets. © 2016 IEEE.
Open Access
An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach
(Hindawi Limited, 2017) Delibalta, I.; Baruh, L.; Kozat, S. S.
We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios. © 2017 Ibrahim Delibalta et al.
Open Access
Shifting network tomography toward a practical goal
(ACM, 2011) Ghita, D.; Karakuş, Can; Argyraki, K.; Thiran, P.
Boolean Inference makes it possible to observe the congestion status of end-to-end paths and infer, from that, the congestion status of individual network links. In principle, this can be a powerful monitoring tool, in scenarios where we want to monitor a network without having direct access to its links. We consider one such real scenario: a Tier-1 ISP operator wants to monitor the congestion status of its peers. We show that, in this scenario, Boolean Inference cannot be solved with enough accuracy to be useful; we do not attribute this to the limitations of particular algorithms, but to the fundamental difficulty of the Inference problem. Instead, we argue that the "right" problem to solve, in this context, is compute the probability that each set of links is congested (as opposed to try to infer which particular links were congested when). Even though solving this problem yields less information than provided by Boolean Inference, we show that this information is more useful in practice, because it can be obtained accurately under weaker assumptions than typically required by Inference algorithms and more challenging network conditions (link correlations, non-stationary network dynamics, sparse topologies).