Discriminant math的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

Applied Statistics + Do the Math!

為了解決Discriminant math的問題，作者Warner, Rebecca M./ White, John K. 這樣論述：

Buy Together and Save Rebecca M. Warner: Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition Applied Statistics: From Bivariate Through Multivariate Techniques, Second Edition, provides a clear introduction to widely used topics in bivariate and multivariate statistics

, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. Author Rebecca M. Warner uses an applied approach that does not require formal mathematics; equations are accompanied by verbal explanations, and students are asked to think about the mea

ning of the equations. Each chapter presents a complete empirical research example to illustrate the application of a specific method, along with a glossary and comprehension questions to help students master each concept. SPSS examples are used throughout the book; however, the conceptual material

will be helpful for users of different programs.John K. White: Do The Math : On Growth, Greed, and Strategic ThinkingHow do pyramid scams work? Are reality television shows fair?Why are sports so uncompetitive? Who really broke the bank in 2009?Our world has become more complicated, and the notion o

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Discriminant math進入發燒排行的影片

智慧心律系統研發:以渾沌積分映射系統為基礎之心律不整檢測系統

為了解決Discriminant math的問題，作者林昱成 這樣論述：

摘要 iABSTRACT ii誌 謝 ivContents vList of Tables viiList of Figures ixChapter 1 Introduction 11.1 Motivation 11.2 Background 11.3 Contributions 61.4 Organization of the Thesis 7Chapter 2 Experiment I - Smart Detection Method for Personal ECG Monitoring 82.1 The Experiment Data Source & Dat

a Processing 92.1.1 The Experiment Data Source 92.1.2 Data Processing 102.1.3 Chaotic-Mapping Integral Network 112.2 Extract Characteristics 142.2.1 Feature Extraction (Euclidean Distance Feature Value) 142.2.2 Feature Extraction (Central Point Distribution) 142.3 Classification 152.3.1 Expe

rimental results-detection of ECG states via method I 162.3.2 Experimental results-detection of ECG states via method II 18Chapter 3 Experiment II- Smart Real-Time Monitoring System for Arrhythmia 233.1 The Experiment Data Source & Data Processing 253.1.1 The Experiment Data Source 253.1.2 Data

Processing 273.2 Double Chaotic-Mapping Integral Network 333.3 Extract Characteristics 373.3.1 Feature Extraction (Euclidean Distance Feature Value) 373.3.2 Feature Extraction (Central Point Distribution Feature Value) 383.4 Classification 383.4.1 Experimental results-detection of ECG states

via method I 403.4.2 Experimental results-detection of ECG states via method II 45Chapter 4 Conclusions and Future Work 524.1 Conclusions 524.2 Future Work 52Reference 54

SAT Ⅱ 數學 Level 1（7套題）

為了解決Discriminant math的問題，作者（美）迪爾Diehl.J.J 編著 這樣論述：

You need a great score on the SAT Mathematics Level1 test to get into your first-choice college, You﹀ve always been good at math, but now you want to be sure you﹀re ready for this tough exam, How can you make certain you﹀re getting the very best preparation available? SAT Subject

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旋轉機械滾動軸承智慧故障診斷模型

為了解決Discriminant math的問題，作者黎長安 這樣論述：

根據測量信號的旋轉機械軸承故障的準確診斷仍然是一個引起廣泛關注的主要問題。目前，越來越多基於機器學習或深度學習理論的智慧故障診斷模型已被開發。這些模型預期能減少對人工的依賴，並增強診斷模型的自動故障檢測。構建智慧故障診斷模型有兩種方法：基於機器學習方法和基於深度學習方法。然而，這兩種方法的有效性仍是一個需要考慮的問題。因此，本研究提出了基於這兩種方法的模型應用於檢測旋轉機械的軸承故障。第一種方法是基於機器學習的智慧軸承故障診斷模型（intelligent bearing fault diagnosis model based on machine learning, IBFDM based

on ML）。此模型包括三個主要部分：特徵提取、特徵選取和特徵分類。旋轉機械的測量信號通過包絡線分析和希爾伯特-黃轉換技術處理以提取潛在特徵。通過基於特徵權重的群體初始化策略、新的群體更新機制以及群體篩選和替換過程對二進制粒子群最佳化進行了增強，創建了一種新的有效特徵選取方法，可提高分類精度並減少數據大小。最優特徵子集分別提供給人工神經網路以及支撐向量機作為最終識別任務。第二種方法是基於深度學習的智慧軸承故障診斷模型（intelligent bearing fault diagnosis model based on deep learning, IBFDM based on DL）。此模型有

兩個主要部分：第一部分是根據每個信號幀的持久性光譜構建圖像數據集。具有殘差網路（residual network, ResNet）結構的卷積類神經網路（convolutional neural network, CNN）被設計用於基於輸入數據的分類是第二部分。持久性光譜是從原始信號的包絡線中提取的。然後，基於短時傅立葉變換構建持久性光譜圖像，呈現出傳統頻譜分析方法未曾給出的每個信號的頻率、振幅和能量隨時間變化的新關係。具有 ResNet 結構的改進 CNN 允許從較低層到較高層直接連接特徵圖，以從包絡信號的持久性光譜圖像中探索判別特徵。這有助於利用低級層中的粒度特徵，這些特徵在傳統 CNN 中

前饋通過相鄰層時可能會遺失。因此，所提出的軸承故障診斷模型的性能在電流信號和振動信號的不同測試平台上得到驗證。模型的效率在軸承電流數據集上實現超過96%的辨識率，在軸承振動數據集上實現超過99%的辨識率。此外，IBFDM based on ML中的新特徵選取方法根據七個基準數據集進行評估，顯示出與其他同級競爭者相當的性能。此外，與其他類型的二維圖像（頻譜圖和尺度圖）和其他最先進的診斷模型相比，IBFDM based on DL的性能更佳。綜上所述，所提出的兩種模型在自動識別旋轉機械健康狀態領域具有很高的可行性。