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Modeling Human Location Data with Mixtures of Kernel Densities
Published on Oct 07, 20141905 Views
Location-based data is increasingly prevalent with the rapid increase and adoption of mobile devices. In this paper we address the problem of learning spatial density models, focusing specifically on
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Chapter list
Modeling Human Location Data with Mixtures of Kernel Densities00:00
Treat locations as events - 100:18
Treat locations as events - 200:34
Treat locations as events - 300:45
Treat locations as events - 400:48
Motivation00:54
Kernel Density Estimation - 101:50
Kernel Density Estimation - 202:05
Kernel Density Estimation - 302:12
Kernel Density Estimation - 402:35
Kernel Density Estimation - 503:03
Kernel Density Estimation - 603:33
Kernel Density Estimation - 703:47
A tale of Two Cities - 103:48
A tale of Two Cities - 204:11
Adaptive Bandwidth KDE - 105:28
Adaptive Bandwidth KDE - 205:57
Adaptive Bandwidth KDE - 306:02
Adaptive Bandwidth KDE - 406:14
Comparing Methods - 106:29
Comparing Methods - 206:58
Comparing Methods - 307:22
Comparing Methods - 407:45
Comparing Methods - 509:08
Mixture of Kernel Densities - 109:40
Mixture of Kernel Densities - 210:35
Evaluation10:50
Evaluation – Log Likelihood - 111:01
Evaluation – Log Likelihood - 211:15
Evaluation – Log Likelihood - 311:33
Evaluation – Log Likelihood - 411:42
Evaluation – Log Likelihood - 511:53
Evaluation – Log Likelihood - 611:59
Evaluation – Fraud Detection - 112:07
Evaluation – Fraud Detection - 212:20
Evaluation – Fraud Detection - 312:40
Evaluation – Fraud Detection - 412:55
Evaluation – Fraud Detection - 513:32
Conclusions13:44
Future Work14:01
Acknowledgements14:25
Q & A14:36