0.25
0.5
0.75
1.25
1.5
1.75
2
An introduction to learning from data streams
Published on Apr 24, 20142765 Views
Related categories
Chapter list
Introduction to Learning from Data Streams00:00
Tribute to Sir Ronald Fisher00:26
Nowadays ...01:42
Data Never Sleeps ...02:18
Scenario - 103:25
Scenario - 204:09
Illustrative Learning Tasks - 105:35
Illustrative Learning Tasks - 205:36
Illustrative Learning Tasks - 305:36
Standard Approach - 108:07
Standard Approach - 209:44
The Data Stream Phenomenon10:11
Data Streams - 111:37
Data Streams - 212:27
Clustering Data Streams13:15
Clustering13:52
K-Means for Streaming Data15:35
Illustrative Example: K-means16:56
Cluster Feature Vector18:10
Micro clusters - 120:14
Micro clusters - 220:35
CluStream21:07
Pyramidal Time Frame22:33
Analysis22:53
MOA24:40
Clustering Time-series Data Streams25:17
Online Divisive-Agglomerative Clustering26:29
Main Algorithm28:25
Feeding ODAC28:26
Similarity Distance30:46
The Expand Operator: Expanding a Leaf31:41
Splitting Criteria32:54
Hoeffding bound36:00
The Expand Operator: Expanding a Leaf37:21
Multi-Time-Windows38:49
The Merge Operator: Change Detection - 140:36
The Merge Operator: Change Detection - 241:04
Properties of ODAC43:14
The Electrical Load Demand Problem43:16
Concept Drift44:21
Introduction44:24
The Nature of Change - 145:25
The Nature of Change - 248:18
The Nature of Change - 348:22
A Generic Model for Adaptive Learning Algorithms53:06
The Page-Hinckley Test - 153:10
The Page-Hinckley Test - 253:55
Analysis54:18
Illustrative Example55:27
Illustrative Problem I55:28
Illustrative Examples56:10
The Top-k Elements Problem56:11
The Space Saving Algorithm57:00
The Space Saving Algorithm: Properties59:27
Master References - 101:00:38
Software01:00:38
Resources01:00:57
Data Stream Management Systems01:01:08
Master References - 201:01:10
Master References - 301:01:20