Notes on codes, projects and everything
2011 February 17
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Collaborative Tagging & Folksonomy
Folksonomy is a neologism of two words, ’folk’ and ’taxonomy’ which describes conceptual structures created by users [4, 5]. A folksonomy is a set of unstructured collaborative usage of tags for content classification and knowledge representation that is popularized by Web 2.0 and social applications [1, 5]. Unlike taxonomy that is commonly used to organize resources to form a category hierarchy, folksonomy is non-hierarchical and non-exclusive [3]. Both content hierarchy and folksonomy can be used together to better content classification.
Random Posts
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Estimating the value of pi with a line of Python
I came across a video on Youtube on Pi day. Coincidently it was about estimating the value of Pi produced by Matt Parker aka standupmaths. While I am not quite interested in knowing the best way to estimate Pi, I am quite interested in the algorithm he showed in the video however. Specifically, I am interested to find out how easy it is to implement in Python.
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Imperfect Reimplementation of Lists in PHP
Long long time ago when I was working with Prolog, I was introduced to list. Unlike arrays in most popular programming languages, we weren’t really able to access to a particular member directly. Every list is constructed in a chain-like structure.
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Sync’ MP4 Tracks through MTP
I’m not sure why Windows Media Player 11 doesn’t like mp4/aac format but not being able to transfer my tracks in mp4 through MTP to my N81 really piss me off. At first I installed Songbird 1.0.0 to try synchronizing the tracks through the MTP addon but it doesn’t even play *.mp4 tracks out of the box under Windows XP SP3 (although it claimed otherwise). Weird thing happens when I was trying to synchronize anyway by having the files renamed to *.mp4 extension and it succeeded but without any meta-tag information.
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Regression with Annoy (cont’d)
After a year and half, a lot of things changed, and annoy also changed the splitting strategy too. However, I always wanted to do a proper follow up to the original post, where I compared boosting to Annoy. I still remember the reason I started that (flawed) experiment was because I found boosting easy.
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Approximate Neighbour Search in Multiple Dimensions
In the previous post, I re-implemented Annoy in 2D with some linear algebra maths. Then I spent some time going through some tutorial on vectors, and expanded the script to handle data in 3D and more. So instead of finding gradient, the perpendicular line in the middle of two points, I construct a plane, and find the distance between it and points to construct the tree.
