en-es
en
0.25
0.5
0.75
1.25
1.5
1.75
2
Learning from Weakly Labeled Data
Published on Aug 26, 20135372 Views
In many machine learning problems, the labels of the training examples are incomplete. These include, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance l
Related categories
Chapter list
Learning from Weakly Labeled Data00:00
Outline00:09
Introduction00:35
Semi-Supervised Learning (SSL)01:14
Multiple Instance Learning (MIL)02:13
Weak Label Information03:45
Clustering05:23
Weak-Label Learning06:20
Existing Algorithms06:46
WellSVM (WEakly LabeLed SVM)08:28
Large-Margin Weak-Label Learning10:59
Large Margin Classifiers13:22
Relax15:08
Tightest Convex Relaxation17:24
How to Solve?19:30
Cutting Plane Algorithm by Label Generation21:15
Properties22:55
Semi-Supervised Learning - 124:45
Semi-Supervised Learning - 226:10
Cutting Plane Algorithm27:04
Issue 1: How to obtain α?27:24
Multiple Label-Kernel Learning29:33
Issue 2: Finding a Violated Label Assignment31:15
Simple Method to Find a Violated Label Assignment33:10
WellSVM for Semi-Supervised Learning35:07
Experiments35:24
Accuracies (5% labeled examples)37:07
CPU Time37:12
Number of WellSVM Iterations37:30
Larger Data Sets37:48
Comparison with Other SSL Benchmarks in the Literature38:27
Comparison with SDP-based Benchmarks38:54
Multiple Instance Learning39:03
Multiple Instance SVM39:46
Optimization Problem41:13
Convex Relaxation41:53
Cutting Plane: Step 142:23
Cutting Plane: Step 242:56
Experiment: CBIR44:11
Multiple Instance Learning for Locating ROIs45:11
Location the Region of Interest (ROI)45:46
Maximum Margin Clustering46:21
Cutting Plane46:52
Experiments47:17
Clustering Accuracies47:30
CPU Time47:37
Large-Scale Experiments (Linear Kernel)48:00
Conclusion48:04
References48:59