Neural Based Orthogonal Data Fitting: The EXIN Neural Networks (Hardcover)
暫譯: 基於神經網絡的正交數據擬合:EXIN神經網絡 (精裝版)
Giansalvo Cirrincione, Maurizio Cirrincione
- 出版商: Wiley
- 出版日期: 2010-11-30
- 定價: $2,980
- 售價: 9.5 折 $2,831
- 語言: 英文
- 頁數: 276
- 裝訂: Hardcover
- ISBN: 0471322709
- ISBN-13: 9780471322702
-
相關分類:
人工智慧、Machine Learning
立即出貨 (庫存 < 4)
商品描述
The literature about neural-based algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, Neural-Based Orthogonal Data Fitting is the first book to start with the MCA problem and arrive at important conclusions about the PCA problem.
The book proposes several neural networks, all endowed with a complete theory that not only explains their behavior, but also compares them with the existing neural and traditional algorithms. EXIN neurons, which are of the authors' invention, are introduced, explained, and analyzed. Further, it studies the algorithms as a differential geometry problem, a dynamic problem, a stochastic problem, and a numerical problem. It demonstrates the novel aspects of its main theory, including its applications in computer vision and linear system identification. The book shows both the derivation of the TLS EXIN from the MCA EXIN and the original derivation, as well as:
Shows TLS problems and gives a sketch of their history and applications
Presents MCA EXIN and compares it with the other existing approaches
Introduces the TLS EXIN neuron and the SCG and BFGS acceleration techniques and compares them with TLS GAO
Outlines the GeTLS EXIN theory for generalizing and unifying the regression problems
Establishes the GeMCA theory, starting with the identification of GeTLS EXIN as a generalization eigenvalue problem
In dealing with mathematical and numerical aspects of EXIN neurons, the book is mainly theoretical. All the algorithms, however, have been used in analyzing real-time problems and show accurate solutions. Neural-Based Orthogonal Data Fitting is useful for statisticians, applied mathematics experts, and engineers.
商品描述(中文翻譯)
正交回歸新理論的呈現
關於基於神經網絡的算法文獻通常專注於主成分分析(PCA),並將次成分分析(MCA)視為一種次要結果。本書《基於神經的正交數據擬合》打破了這一模式,首次從MCA問題出發,並對PCA問題得出重要結論。
本書提出了幾種神經網絡,這些網絡都擁有完整的理論,不僅解釋了它們的行為,還將它們與現有的神經和傳統算法進行比較。作者發明的EXIN神經元被引入、解釋和分析。此外,本書將算法研究作為微分幾何問題、動態問題、隨機問題和數值問題。它展示了其主要理論的新穎方面,包括在計算機視覺和線性系統識別中的應用。本書展示了從MCA EXIN推導TLS EXIN的過程及其原始推導,並且:
展示TLS問題並簡要介紹其歷史和應用
呈現MCA EXIN並將其與其他現有方法進行比較
介紹TLS EXIN神經元及SCG和BFGS加速技術,並將其與TLS GAO進行比較
概述GeTLS EXIN理論,以推廣和統一回歸問題
建立GeMCA理論,從識別GeTLS EXIN作為一個廣義特徵值問題開始
在處理EXIN神經元的數學和數值方面時,本書主要是理論性的。然而,所有算法都已用於分析實時問題並顯示出準確的解決方案。《基於神經的正交數據擬合》對統計學家、應用數學專家和工程師都非常有用。