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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭卜壬(Pu-Jen Cheng) | |
| dc.contributor.author | Cheng-De Lin | en |
| dc.contributor.author | 林承德 | zh_TW |
| dc.date.accessioned | 2022-11-23T09:30:46Z | - |
| dc.date.available | 2021-08-23 | |
| dc.date.available | 2022-11-23T09:30:46Z | - |
| dc.date.copyright | 2021-08-23 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-16 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80186 | - |
| dc.description.abstract | 自適應提升演算法，是提升演算法中一個非常經典的傑作，它的驚人能力已經被驗證多年。它會透過將多個弱分類器有順序性的訓練，並使用先前訓練中所得到的錯誤來幫助新的分類器，盡可能的使其不要再犯相同的錯誤，並且能夠使這些訓練好的弱分類器同心協力，互相互補，進而成為一個更強的分類器。然而隨著時間的推移，近幾年來陸續有各式各樣關於機器學習的不同想法被提出，或許乍看之下這些新的想法與自適應提升演算法並無關連，但我們認為其中有些其實是可以融合進原本的自適應提升演算法中，並且讓其變得更好的。 舉例來說，使用多個不同的特徵空間來訓練，以及近年來非常火紅的資料增強技術，在我們的認知中，便還未有人將其與自適應提升演算法一同討論。因此我們在本篇論文中提出了一個可以使用多個不同特徵空間，以及加入了資料增強來輔助的自適應提升演算法架構。並且，我們分別對圖像，文字等不同屬性的資料在多個資料集中進行了實驗，而透過實驗我們發現，使用多個不同的特徵空間，確實能夠對分類帶來助益，同時資料增強的技術也能輔助自適應提升演算法，使其有更好的結果。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-23T09:30:46Z (GMT). No. of bitstreams: 1 U0001-2206202115271900.pdf: 7909218 bytes, checksum: e13971edbb35808209537fde7559f488 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | Verification Letter from the Oral Examination Committee I Acknowledgements ii 摘要 iii Abstract iv Contents v List of Figures vii List of Tables ix Denotation x Chapter 1. Introduction 1 Chapter 2. Related Work 7 2.1 Adaptive Boosting 7 2.2 Multiple Feature Spaces Learning 8 2.3 Data Augmentation 9 Chapter 3. Proposed Method 11 3.1 Main Challenges 11 3.2 Model Overview 12 3.3 Single Estimator 14 3.4 Evaluation and Re-Weighting 15 3.5 Data Augmentation Block 17 3.6 Training and Testing 19 Chapter 4. Experiments 21 4.1 Datasets 21 4.2 Experiment Settings 24 4.3 Image Classification 25 4.4 Sentiment Analysis 28 4.5 Case Studies 29 4.6 Feature Space Comparison 31 4.7 Data Augmentation Comparison 32 4.8 Inheritance Rates 37 Chapter 5. Conclusions 40 5.1 Conclusions 40 5.2 FutureWorks 40 References 42 | |
| dc.language.iso | en | |
| dc.subject | 資料增強 | zh_TW |
| dc.subject | 圖像分類 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | 自適應提升 | zh_TW |
| dc.subject | 多特徵空間 | zh_TW |
| dc.subject | multi-feature spaces | en |
| dc.subject | Image Classification | en |
| dc.subject | CNN | en |
| dc.subject | AdaBoost | en |
| dc.subject | data augmentation | en |
| dc.title | 結合資料增強與多特徵空間的自適應提升模型 | zh_TW |
| dc.title | AdaBoost with Data Augmentation in Multiple Feature Spaces | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳信希(Hsin-Tsai Liu),曾新穆(Chih-Yang Tseng),陳建錦 | |
| dc.subject.keyword | 資料增強,多特徵空間,自適應提升,卷積神經網路,圖像分類, | zh_TW |
| dc.subject.keyword | data augmentation,multi-feature spaces,AdaBoost,CNN,Image Classification, | en |
| dc.relation.page | 46 | |
| dc.identifier.doi | 10.6342/NTU202101094 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2021-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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