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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 2Rabbany khorasgani, Reihaneh
- 2Sacharuk, Edward, 1948-
- 2Sharifi, AmirAli
- 54Machine Learning
- 48Reinforcement Learning
- 37Artificial Intelligence
- 31Machine learning
- 20Image processing. Digital techniques.
- 18Artificial intelligence
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Fall 2010
A cluster-based router is a new router architecture that is composed of a cluster of commodity processing nodes interconnected by a high-speed and low-latency network. It inherits packet processing extensibility from the software router, and forwarding performance scalability from clustering. In...
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Spring 2012
Mirian HosseinAbadi, MahdiehSadat
In this thesis we propose a computational model of animal behavior in spatial navigation, based on reinforcement learning ideas. In the field of computer science and specifically artificial intelligence, replay refers to retrieving and reprocessing the experiences that are stored in an abstract...
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Fall 2013
The issue-tracking systems used by software projects contain issues or bugs written by a wide variety of bug reporters, with different levels of knowledge about the system under development. Typically, reporters lack the skills and/or time to search the issue-tracking system for similar issues...
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Fall 2012
Recent proliferation of low-cost and lightweight GPS tracking devices led to a large increase in the amounts of collected mobility data. The rapidly emerging field of location-based services requires accurate and informative knowledge mining from these large quantities of data. One such mobility...
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Spring 2022
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the unknown task distribution. In this work, focusing on fixed design linear regression with Gaussian noise and a...
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Fall 2013
In wireless sensor networks, sensor nodes may fail due to energy depletion or physical damage. To recover the data of a failed node, we propose a fault recovery scheme which enables the remaining alive sensor nodes to use the redundant information with regard to the failed node to fulfill the...
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Fall 2019
Nowadays, the volume of collected data and the size of datasets raise various challenges in the field of data mining. One of such challenges is to, given a dataset, monitor a set of data points and its changes over a period of time. Previously, this monitoring has been done using pattern...
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A Framework for Associating Mobile Devices to Individuals Based on Identification of Motion Events
DownloadFall 2020
The ubiquity of the Internet-of-Things (IoT) devices in everyday life allows various sensors to be utilized in networked systems for solving a number of real-world problems. Models utilizing specific sensing modalities achieve impressive performance in understanding human activity and are used in...
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Spring 2021
Cavalcante Araujo Neto, Antonio
HDBSCAN* is a hierarchical density-based clustering method that requires a single parameter mpts, a smoothing factor that implicitly influences which clusters are more detectable in the resulting clustering hierarchy. While a small change in mpts typically leads to a small change in the...
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Spring 2022
The world offers unprecedented amounts of data in real-world domains, from which we can develop successful decision-making systems. It is possible for reinforcement learning (RL) to learn control policies offline from such data but challenging to deploy an agent during learning in safety-critical...