9/29/2008

Perceptron Learning Rule

來記幾個本來不懂的地方。

1. For "linear splitable” data

2. why expand matrix ?
since the target function is:  g^t (X) = w^t (x) - θ^t 
to simplify the computing,  extend X = {-1, x_1, x_2, ... , X_d} and W = {θ, w_1, w_2, ..., w_d}, so X‧W = Σ_{i=1}^d {w_i * x_i }  - θ = w‧x - θ

3. update function?
the origin function is 
if X ε P , and W_{t-1} ‧X <= 0 , W_t = W_{t-1} + X
else if X ε N, and W_{t-1}‧X >0, W_t = W_{t-1} - X
else W_t = W_{t-1}

we can simplify the first two function by 
if y= 1 , and W_{t-1} ‧X <= 0 , W_t = W_{t-1} + X = W_{t-1} + y‧X
else if y = -1, and W_{t-1}‧X >0, W_t = W_{t-1} - X = W_{t-1} + y‧X
→ if (sign(W‧X) != y )  W_t = W_{t-1} + y‧X


User Study

補記一下上次去Hao meeting的重點,這次主要是投CHI的 經驗分享,尤其是在最後做user study的 時候,有不少寶貴的經驗可以讓我們參考。

1. User study is Expensive
 I think most problems dicussed are all around this. Since it is too expensive, we have to make sure everything is done before the study.  In another word,  there are something we should bare in mind before the test. 

(a) Never Debugging Via User Study. 
(b) Run an complete Test Before The User Study
(c) Carefully Choose The Participants.
(d) Clearly Define the Test Rule, and Make Sure All The Participants Understand.

2. Log
(a) Try to record any information in the study.
(b) Record them in a proper form. 

For logging, there are some related issue we've discussed in the meeting:
Server-based: be careful of the internet problem. 
Client-based: how to merge data from different client.


--
好像還有一些其他的,不過我有點忘記了T.T

9/23/2008

attribute to be displayed

1. weight
2. degree
3. relation(belongs to, relates to)

改版demo

Demo link

覺得還是flash做出來的效果比java好,所以就把程式改成flash版了

不過初版還少很多東西

1. clustering algorithm →不知道用哪一個clustering比較好,目前implement的很沒有效率,點多很容易就炸,但我又要限制在3組內儘可能大…所以實在不知道要用哪一個演算法比較好

2. group issue → 想要有階層架構,如果單就一個人的tag的資料來呈現,hierarchy的效果不是那麼明顯,但如果使用group的階層又怕會有一字多義造成階層錯誤的情況。

3. remove overlap: 參考[Using Spring Algorithms to Remove Node Overlapping] 的作法來移除重疊的點。這篇主要的原理是以變動的k來調整edge的長度


4. 另一大目標:標示出"使用者自訂字",例如我自己會把課程網站都加上一個標籤叫course,把研究相關的都加上一個research,要怎麼從tag裡面找到這種對使用者有特別意義的詞呢?


9/18/2008

result - for more tags

寫好了界接delicious的版本
暫時不會再有sample量太小的問題,但卻出現了更大的問題T.T



只有86個點,看起來還ok



100個點看起來也還可以接受



209個點畫面就已經爆掉了


所以接下來要調整的目標應該是把部份tag再做clustering
目前有兩個方向:
一是直接對data作處理!這邊應該要往semantic來做:

二是對graph作處理,上次剛好有找到一篇相關的paper:
A Framework of Filtering, Clustering and Dynamic Layout Graphs for Visualization

semantic的話應該要結合conceptNet跟之前學姊們parse下來的delicious data
graph的話就要先念完paper才知道可不可行了,雖然它在introduction裡有提到他們寫好一個demo的framework, 但google卻沒有相關的resource可以先測試效果好不好。

不過目前最想先做的是把現在的版本重新implement成flex版,然後一些之前偷懶用人家已經implement好的資料再tune過,之後要繼續發展才會更迅速。

9/17/2008

Brief Summary of What I am goint to do

I would like to focus my study on "Tag querry visualization". In fact, most tag querry system such as word cloud doesn't really match human's need. When the number of tag increases, the more difficult to find out the exact document via tag. 

Therefore, I wish the query system must be based on "The strategy of Human Tag". 
So, it's more about psychology, and I'm going to take the couse this semester. 

Moreover, i would like to display tags with their relation.  The relation may be based on semantic meaning. Therefore, machine learning is also important class to take for me. 

Finally, to render the view,  I'll need to learn more about graph drawing. 

These are three main task for me in this semester. 

懷舊小遊戲 - zebra

今天上完ai課時忽然想到高中時候玩的黑白棋遊戲 zebra

從前一直覺得黑白棋超簡單的,這是我第一次發現電腦可以強大到這種地步!!印象中,我好像在某一級就只贏了一場!之後就再也沒有贏過。剛剛google了一下,發現這後續還有好多變異版,也許有空應該來挑戰一下看看我究竟退化了多少xD

9/16/2008

Webpages as Graphs

WebPages as Graphs 是一個把網站上的DOM轉成用圖片表示的,上面是我的blog首頁所畫出來的圖片!說實話,很有趣但不是很實用!

Try it:

9/15/2008

知覺心理學9/16 上課筆記

前面第一節課先介紹課程大網,然後是introduction的部份。

1. Mind = Brain ??

2. Flow: Sensation → Perception → Action

3. three kinds of view: 

 (1) Objective客觀:Perception 的正為了真實反映世界  (被Kanizsa triangle推翻:不完全單純反映)

 (2) Subjective主觀:我們理解到的世界是腦中所創造出來的世界

 (3) Synthetic 綜合:因為nerve system本身的限制(ex. 眼睛只能接受某些頻率的光),但為了正確而快速的反映真實世界,所以結合subject的方式來呈現。


Kanizsa Triangle: 中央的三角形事實上沒有被任何東西defined,但我們仍然可以感知這個浮出的三角形。


4. Helmholtz:Unconscious inference

5. What is reality?
Physical  Reality → Model → Psychological Reality
☆However, All Model is not perfect, but the problem is, how can we make our conclusion with these unperfect models.

p.s. 中文課本導讀