Generative model vs Discriminative model

Generative model (e.g., Gaussian Mixture Model)

Idea:
building model from the observation
P(x|c) - giving class c, guess the data point x.

Pros:
get the underlying idea of what the class is built on

Cons:
1. very expensive, lots of parameter
2. needs lots of data

Discriminative model (e.g. SVM)

Idea:
differentiate different classes
P(c|x) - giving data point x, guess the class c.

Pros:
easy to model

Cons: 
to classify but not to generate the data/observation back

Example:
Let a child go to the zoo to see elephant,
when he is back, giving a set of horse and elephant, if he can differentiate elephant from horse only, what he learnt is the discriminative model.
But if he can draw the elephant, what he learnt is the generative model.

Resource from: http://www.youtube.com/watch?v=OWJ8xVGRyFA&noredirect=1

Sensor Network Conferences

This is collected when talking with a profs

First: mobisys sensys  mobicom secon (very specific, 20-30 papers)
Second: infocom and icdcs (broad, although may have smaller acceptance rate than the first one)


Other related conference
Sigmetrics

Partition and Timed Label

发现自己最喜欢想idea,尤其是能赚钱的idea.
但是有时候，一边想，一边想，时间就过去了。

正确来说应该是partition时间，然后把task 放进去。
想idea只占其中一块partition, 其他的是execution, 和planning

还有很重要的一点
任务必须timed label,避免proscastination.
最不喜欢的有时候是execution,但是还是必须把它做下来的。