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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59090
Title: | 大規模線性分類資料低階多項式映射中雜湊函數之應用 Hash Functions for Polynomial Feature Mapping in Large Scale Linear Classification |
Authors: | Xiaocong Zhou 周驍聰 |
Advisor: | 林智仁(Chih-Jen Lin) |
Keyword: | 低階多項式映射,雜湊函數, low-degree polynomial mapping,hash functions, |
Publication Year : | 2017 |
Degree: | 碩士 |
Abstract: | Nonlinear mappings have long been used in data classification to handle linearly inseparable problems. Low-degree polynomial mappings are a widely used one among them, which enjoys less time and space consumption and may sometimes achieve accuracy close to that of using highly nonlinear kernels. However, the explicit form of polynomially mapped data for large data sets can also meet memory or computational difficulties. To solve this, hash functions like murmur and fnv hash are used in some packages like vowpal wabbit to have flexible memory usage. In this thesis, we propose a new hash function which is faster and could achieve the same performance. The results are validated in experiments on many datasets. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/59090 |
DOI: | 10.6342/NTU201701597 |
Fulltext Rights: | 有償授權 |
Appears in Collections: | 資訊工程學系 |
Files in This Item:
File | Size | Format | |
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ntu-106-1.pdf Restricted Access | 241.91 kB | Adobe PDF |
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