Dimensionality Reduction with Unsupervised Nearest Neighbors
Oliver Kramer
2013๋
5์ ยท Springer Science & Business Media
eBook
132
ํ์ด์ง
report๊ฒ์ฆ๋์ง ์์ ํ์ ๊ณผ ๋ฆฌ๋ทฐ์
๋๋ค. ย ์์ธํ ์์๋ณด๊ธฐ
eBook ์ ๋ณด
This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.
์ด eBook ํ๊ฐ
์๊ฒฌ์ ์๋ ค์ฃผ์ธ์.
์ฝ๊ธฐ ์ ๋ณด
์ค๋งํธํฐ ๋ฐ ํ๋ธ๋ฆฟ
Android ๋ฐ iPad/iPhone์ฉ Google Play ๋ถ ์ฑ์ ์ค์นํ์ธ์. ๊ณ์ ๊ณผ ์๋์ผ๋ก ๋๊ธฐํ๋์ด ์ด๋์๋ ์จ๋ผ์ธ ๋๋ ์คํ๋ผ์ธ์ผ๋ก ์ฑ
์ ์ฝ์ ์ ์์ต๋๋ค.
๋
ธํธ๋ถ ๋ฐ ์ปดํจํฐ
์ปดํจํฐ์ ์น๋ธ๋ผ์ฐ์ ๋ฅผ ์ฌ์ฉํ์ฌ Google Play์์ ๊ตฌ๋งคํ ์ค๋์ค๋ถ์ ๋ค์ ์ ์์ต๋๋ค.
eReader ๋ฐ ๊ธฐํ ๊ธฐ๊ธฐ
Kobo eReader ๋ฑ์ eBook ๋ฆฌ๋๊ธฐ์์ ์ฝ์ผ๋ ค๋ฉด ํ์ผ์ ๋ค์ด๋ก๋ํ์ฌ ๊ธฐ๊ธฐ๋ก ์ ์กํด์ผ ํฉ๋๋ค. ์ง์๋๋ eBook ๋ฆฌ๋๊ธฐ๋ก ํ์ผ์ ์ ์กํ๋ ค๋ฉด ๊ณ ๊ฐ์ผํฐ์์ ์์ธํ ์๋ด๋ฅผ ๋ฐ๋ฅด์ธ์.
