WIP: checkpoint methodology

latex/bibliography.bib

@@ -14,6 +14,113 @@
   file = {C:\Users\damar\Zotero\storage\5WG6DL7B\Abdeljaber et al. - 2017 - Real-time vibration-based structural damage detect.pdf}
 }
 
+@book{geron2019,
+  title = {Hands-on Machine Learning with {{Scikit-Learn}}, {{Keras}}, and {{TensorFlow}}: Concepts, Tools, and Techniques to Build Intelligent Systems},
+  shorttitle = {Hands-on Machine Learning with {{Scikit-Learn}}, {{Keras}}, and {{TensorFlow}}},
+  author = {Géron, Aurélien},
+  date = {2019},
+  edition = {Second edition},
+  publisher = {O'Reilly},
+  location = {Beijing Boston Farnham Sebastopol Tokyo},
+  abstract = {Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks--Scikit-Learn and TensorFlow--author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets},
+  isbn = {978-1-4920-3264-9 978-1-4920-3261-8},
+  langid = {english},
+  pagetotal = {1}
+}
+
+@inproceedings{Kohavi1995ASO,
+  title={A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection},
+  author={Ron Kohavi},
+  booktitle={International Joint Conference on Artificial Intelligence},
+  year={1995},
+  url={https://api.semanticscholar.org/CorpusID:2702042}
+}
+
+@article{JMLR:v9:vandermaaten08a,
+  author  = {Laurens van der Maaten and Geoffrey Hinton},
+  title   = {Visualizing Data using t-SNE},
+  journal = {Journal of Machine Learning Research},
+  year    = {2008},
+  volume  = {9},
+  number  = {86},
+  pages   = {2579--2605},
+  url     = {http://jmlr.org/papers/v9/vandermaaten08a.html}
+}
+
+@article{JMLR:v22:20-1061,
+  author  = {Yingfan Wang and Haiyang Huang and Cynthia Rudin and Yaron Shaposhnik},
+  title   = {Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization},
+  journal = {Journal of Machine Learning Research},
+  year    = {2021},
+  volume  = {22},
+  number  = {201},
+  pages   = {1-73},
+  url     = {http://jmlr.org/papers/v22/20-1061.html}
+}
+
+@article{CC01a,
+ author = {Chang, Chih-Chung and Lin, Chih-Jen},
+ title = {{LIBSVM}: A library for support vector machines},
+ journal = {ACM Transactions on Intelligent Systems and Technology},
+ volume = {2},
+ issue = {3},
+ year = {2011},
+ pages = {27:1--27:27},
+ note =	 {Software available at \url{http://www.csie.ntu.edu.tw/~cjlin/libsvm}}
+}
+
+@inproceedings{Hsu2009APG,
+  title={A Practical Guide to Support Vector Classification},
+  author={Chih-Wei Hsu and Chih-Chung Chang and Chih-Jen Lin},
+  year={2009},
+  url={https://api.semanticscholar.org/CorpusID:267925897}
+}
+
+@article{hsu2002,
+  title = {A Comparison of Methods for Multiclass Support Vector Machines},
+  author = {Hsu, Chih-Wei and Lin, Chih-Jen},
+  date = {2002},
+  journaltitle = {IEEE transactions on neural networks},
+  shortjournal = {IEEE Trans Neural Netw},
+  volume = {13},
+  number = {2},
+  eprint = {18244442},
+  eprinttype = {pmid},
+  pages = {415--425},
+  issn = {1045-9227},
+  doi = {10.1109/72.991427},
+  abstract = {Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much larger optimization problem is required so up to now experiments are limited to small data sets. In this paper we give decomposition implementations for two such "all-together" methods. We then compare their performance with three methods based on binary classifications: "one-against-all," "one-against-one," and directed acyclic graph SVM (DAGSVM). Our experiments indicate that the "one-against-one" and DAG methods are more suitable for practical use than the other methods. Results also show that for large problems methods by considering all data at once in general need fewer support vectors.},
+  langid = {english}
+}
+
+@article{JMLR:v18:16-174,
+  title = {Empirical Evaluation of Resampling Procedures for Optimising {{SVM}} Hyperparameters},
+  author = {Wainer, Jacques and Cawley, Gavin},
+  date = {2017},
+  journaltitle = {Journal of Machine Learning Research},
+  volume = {18},
+  number = {15},
+  pages = {1--35},
+  url = {http://jmlr.org/papers/v18/16-174.html}
+}
+
+
+@article{diao2023,
+  title = {Structural Damage Identification Based on Variational Mode Decomposition–{{Hilbert}} Transform and {{CNN}}},
+  author = {Diao, Yansong and Lv, Jianda and Wang, Qiuxiao and Li, Xingjian and Xu, Jing},
+  date = {2023-10},
+  journaltitle = {Journal of Civil Structural Health Monitoring},
+  shortjournal = {J Civil Struct Health Monit},
+  volume = {13},
+  number = {6--7},
+  pages = {1415--1429},
+  issn = {2190-5452, 2190-5479},
+  doi = {10.1007/s13349-023-00715-3},
+  url = {https://link.springer.com/10.1007/s13349-023-00715-3},
+  urldate = {2025-05-19},
+  langid = {english},
+}
+
 @article{zhao2019,
   title = {Bolt Loosening Angle Detection Technology Using Deep Learning},
   author = {Zhao, Xuefeng and Zhang, Yang and Wang, Niannian},