相關主題
商品描述
A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis.
Features
- Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory
- Focuses on methodology and results rather than formal proofs
- Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO)
- Uses concrete and realistic data analysis examples to guide the reader
- Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges
商品描述(中文翻譯)
在統計分析中，特別是在大數據時代，一項核心任務是將靈活的、高維度的和非線性的模型擬合到噪聲數據上，以捕捉有意義的模式。這通常會導致具有挑戰性的非線性和非凸全局優化問題。在大數據應用中必須處理的大量數據進一步增加了這些問題的難度。《群體智慧方法在統計回歸中的應用》描述了計算群體智慧（SI）領域的方法，以及這些方法如何用來克服統計分析中遇到的優化瓶頸。
特點
- 提供統計數據分析和隨機優化理論中的關鍵結果的簡短、自足的概述
- 專注於方法論和結果，而非正式證明
- 深入回顧SI方法，特別關注粒子群優化（PSO）
- 使用具體且現實的數據分析範例來指導讀者
- 包含調整PSO以在現實世界數據分析挑戰中獲得良好性能的實用技巧和建議
作者簡介
Soumya D. Mohanty, Professor of Physics at the University of Texas Rio Grande Valley, completed his PhD degree in 1997 at the Inter-University Center for Astronomy and Astrophysics, India. He subsequently held post-doctoral positions at Northwestern University, Penn State, and the Max-Planck Institute for Gravitational Physics. He was also a visiting scholar with the LIGO project at Caltech. Mohanty's research has focused on solving some of the important data analysis challenges faced in Gravitational Wave (GW) astronomy across all observational frequency bands. These include non-parametric regression of very weak signals in noisy data, high-dimensional non-linear parametric regression, time series classification, and analysis of data from large heterogeneous sensor arrays. Mohanty's work has been funded by grants from the Research Corporation, the U.S. National Science Foundation, and NASA.
作者簡介(中文翻譯)
Soumya D. Mohanty，德克薩斯大學里奧格蘭德谷分校的物理學教授，於1997年在印度的國際天文與天體物理中心獲得博士學位。隨後，他在西北大學、賓州州立大學和馬克斯·普朗克重力物理研究所擔任博士後研究員。他還曾在加州理工學院的LIGO項目擔任訪問學者。Mohanty的研究專注於解決重力波（Gravitational Wave, GW）天文學中面臨的一些重要數據分析挑戰，這些挑戰涵蓋了所有觀測頻率範圍。這些挑戰包括在噪聲數據中對非常微弱信號的非參數回歸、高維非線性參數回歸、時間序列分類以及來自大型異質傳感器陣列的數據分析。Mohanty的工作得到了研究公司、美國國家科學基金會和NASA的資助。
