Difference between revisions of "User talk:Xxiong"
PastStudents (talk | contribs) |
PastStudents (talk | contribs) |
||
| Line 8: | Line 8: | ||
1. A revisit of boosting. | 1. A revisit of boosting. | ||
| − | 2. | + | 2. Extend a stacked hierarchical model recently developed for vision tasks and apply it |
| + | to | ||
| + | |||
== Motivation == | == Motivation == | ||
1. | 1. | ||
| Line 16: | Line 18: | ||
as in stacking. | as in stacking. | ||
| − | 2. | + | 2. The intuition of stacked hierarchical model is that |
| + | predictions from one level of the hierarchy should help to predict the entities in the level above or below. | ||
| + | Different from LDA, this model can only be used in a supervised mode. | ||
== Dataset == | == Dataset == | ||
Revision as of 00:19, 29 September 2010
Contents
Project Proposal I
Team Members
Xuehan Xiong [xxiong@andrew.cmu.edu]
Goal
1. A revisit of boosting.
2. Extend a stacked hierarchical model recently developed for vision tasks and apply it to
Motivation
1. In the traditional boosting, within each iteration the mis-classified samples are weighted more in the next round. However, these errors are made from training data. In my algorithm, I will give more weight to the data that are mis-labeled from cross-validation process, as in stacking.
2. The intuition of stacked hierarchical model is that predictions from one level of the hierarchy should help to predict the entities in the level above or below. Different from LDA, this model can only be used in a supervised mode.
Dataset
Superpowers
Experience with CRF and stacking in the domain of computer vision.
What question you want to answer
1. I want to know whether the proposed algorithm will outperform the traditional Ada-boost.
2.
