Now showing items 1-6 of 6
POLICY ADVICE, NON-CONVEX AND DISTRIBUTED OPTIMIZATION IN REINFORCEMENT LEARNING
Transfer learning is a method in machine learning that tries to use previous training knowledge to speed up the learning process. Policy advice is a type of transfer learning method where a student agent is able to learn ...
Supervised Learning in Dynamic Streaming Graphs
With the emergence of networked data, graph classification has received considerable interest during the past years. Most approaches to graph classification focus on designing effective kernels to compute similarities for ...
Computational approaches for the prediction of apicoplast-targeted proteins
Motivation:The cells of eukaryotic organisms contain subunits called organelles. The apicoplast is a unique organelle found in a group of parasites, known as Apicomplexa, that are responsible for a wide range of serious ...
Investigating the Relationship between Sleep and Wake Behavior using Machine Learning and Smart Home Sensors
Smart home technologies offer a unique opportunity to monitor individuals in a non-invasive manner. Because of this, they provide an ideal opportunity for pervasive healthcare. Smart homes provide the resident the freedom ...
DYNAMIC ADAPTATION OF RECOGNITION ALGORITHMS ON WEARABLES WITH MINIMAL HUMAN SUPERVISION
Wearable sensors utilize machine learning algorithms to infer important events such as behavioral routine and health status of their end-users from time-series sensor data. A major obstacle in the large-scale utilization ...
REAL-TIME DETECTION OF MULTI-SCALE CHANGES IN SMART HOMES
Change Point Detection (CPD) is the problem of discovering time points at which the behavior of a time series changes abruptly. Such abrupt changes may represent transitions that occur between states. Tracking these changes ...