194 lines
10 KiB
BibTeX
194 lines
10 KiB
BibTeX
@article{bennettDecisionTreeConstruction1992,
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title = {Decision Tree Construction via Linear Programming},
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author = {Bennett, Kristin},
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year = 1992,
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month = jan,
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journal = {Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society Conference, Utica, Illinois}
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}
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@book{bishopPatternRecognitionMachine2006,
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title = {Pattern Recognition and Machine Learning},
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author = {Bishop, Christopher M.},
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year = 2006,
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series = {Information Science and Statistics},
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publisher = {Springer},
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address = {New York},
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isbn = {978-0-387-31073-2},
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langid = {english},
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lccn = {006.4},
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file = {/home/lars/Zotero/storage/9H5W9BGC/Bishop - 2006 - Pattern recognition and machine learning.pdf}
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}
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@article{bognerRegularizationOptimizationAll2025,
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title = {Regularization and {{Optimization}} Is {{All You Need}}?},
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author = {Bogner, Lars},
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year = 2025,
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month = oct,
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journal = {FYS-STK4155 Reports},
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volume = {2025}
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}
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@misc{elstnerLectureMachineLearning2025,
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type = {{Lecture}},
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title = {{Lecture: Machine Learning for Chemistry}},
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author = {Elstner, Marcus and Kubar, Tomas},
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year = 2025,
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month = may,
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address = {Karlsruhe Institute for Technology, Karlsruhe},
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langid = {ngerman},
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file = {/home/lars/Zotero/storage/5LSLJMK8/Elstner and Kubar - 2025 - Lecture Machine Learning for Chemistry.pdf}
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}
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@book{goodfellowDeepLearning2016,
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title = {Deep {{Learning}}},
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author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
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year = 2016,
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publisher = {MIT Press}
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}
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@article{harrisArrayProgrammingNumPy2020,
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title = {Array Programming with {{NumPy}}},
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author = {Harris, Charles R. and Millman, K. Jarrod and van der Walt, St{\'e}fan J. and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and van Kerkwijk, Marten H. and Brett, Matthew and Haldane, Allan and del R{\'i}o, Jaime Fern{\'a}ndez and Wiebe, Mark and Peterson, Pearu and {G{\'e}rard-Marchant}, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
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year = 2020,
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month = sep,
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journal = {Nature},
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volume = {585},
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number = {7825},
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pages = {357--362},
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publisher = {{Springer Science and Business Media LLC}},
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doi = {10.1038/s41586-020-2649-2}
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}
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@book{hastieElementsStatisticalLearning2009,
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title = {The {{Elements}} of {{Statistical Learning}}},
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author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
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year = 2009,
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series = {Springer {{Series}} in {{Statistics}}},
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publisher = {Springer},
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address = {New York, NY},
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doi = {10.1007/978-0-387-84858-7},
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urldate = {2025-09-22},
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copyright = {http://www.springer.com/tdm},
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isbn = {978-0-387-84857-0 978-0-387-84858-7},
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keywords = {Averaging,Boosting,classification,clustering,data mining,machine learning,Projection pursuit,Random Forest,supervised learning,Support Vector Machine,unsupervised learning},
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file = {/home/lars/Zotero/storage/D3N4DVY9/Hastie et al. - 2009 - The Elements of Statistical Learning.pdf}
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}
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@article{hunterMatplotlib2DGraphics2007,
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title = {Matplotlib: {{A 2D}} Graphics Environment},
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author = {Hunter, J. D.},
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year = 2007,
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journal = {Computing in Science \& Engineering},
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volume = {9},
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number = {3},
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pages = {90--95},
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publisher = {IEEE COMPUTER SOC},
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doi = {10.1109/MCSE.2007.55},
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abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.}
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}
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@misc{kieslerLectureModernMethods2025,
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type = {Lecture},
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title = {Lecture: {{Modern Methods}} of {{Data Analysis}}},
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author = {Kiesler, Jan},
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year = 2025,
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month = may,
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address = {Karlsruhe Institute for Technology, Karlsruhe}
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}
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@misc{kingmaAdamMethodStochastic2017,
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title = {Adam: {{A Method}} for {{Stochastic Optimization}}},
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shorttitle = {Adam},
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author = {Kingma, Diederik P. and Ba, Jimmy},
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year = 2017,
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month = jan,
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number = {arXiv:1412.6980},
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eprint = {1412.6980},
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primaryclass = {cs},
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publisher = {arXiv},
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doi = {10.48550/arXiv.1412.6980},
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urldate = {2025-11-04},
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abstract = {We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.},
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archiveprefix = {arXiv},
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keywords = {Computer Science - Machine Learning},
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file = {/home/lars/Zotero/storage/X65FYFZX/Kingma and Ba - 2017 - Adam A Method for Stochastic Optimization.pdf;/home/lars/Zotero/storage/HPVMMQBU/1412.html}
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}
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@misc{mostafaBreastCancerPrediction,
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title = {Breast {{Cancer Prediction}} (F1 = 0.99, {{AUC}} = 0.99)},
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author = {Mostafa, Omar},
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urldate = {2025-11-03},
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abstract = {Explore and run machine learning code with Kaggle Notebooks \textbar{} Using data from Breast Cancer Wisconsin (Diagnostic) Data Set},
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howpublished = {https://kaggle.com/code/omarmostafataha/breast-cancer-prediction-f1-0-99-auc-0-99},
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langid = {english},
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file = {/home/lars/Zotero/storage/NFAZ887D/breast-cancer-prediction-f1-0-99-auc-0-99.html}
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}
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@misc{NeuralNetworksTikZnet2024,
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title = {Neural Networks -- {{TikZ}}.Net},
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year = 2024,
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month = apr,
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urldate = {2025-11-01},
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abstract = {Some examples of neural network architectures: deep neural networks (DNNs), a deep convolutional neural network (CNN), an autoencoders (encoder+decoder), and the illustration of an activation function in neurons. Basic idea The full LaTeX code at the bottom of this post uses the listofitems library, so one can pre-define an array of the number of nodes\dots},
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langid = {american},
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file = {/home/lars/Zotero/storage/7F7GGAEU/neural_networks.html}
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}
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@article{pedregosaScikitlearnMachineLearning2011,
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title = {Scikit-Learn: {{Machine Learning}} in {{Python}}},
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author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
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year = 2011,
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journal = {Journal of Machine Learning Research},
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volume = {12},
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pages = {2825--2830}
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}
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@inproceedings{streetNuclearFeatureExtraction1993,
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title = {Nuclear Feature Extraction for Breast Tumor Diagnosis},
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booktitle = {{{IS}}\&{{T}}/{{SPIE}}'s {{Symposium}} on {{Electronic Imaging}}: {{Science}} and {{Technology}}},
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author = {Street, W. N. and Wolberg, W. H. and Mangasarian, O. L.},
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editor = {Acharya, Raj S. and Goldgof, Dmitry B.},
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year = 1993,
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month = jul,
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pages = {861--870},
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address = {San Jose, CA},
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doi = {10.1117/12.148698},
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urldate = {2025-11-04},
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abstract = {Interactive image processing techniques, along with a linear-programming-based inductive classifier, have been used to create a highly accurate system for diagnosis of breast tumors. A small fraction of a fine needle aspirate slide is selected and digitized. With an interactive interface, the user initializes active contour models, known as snakes, near the boundaries of a set of cell nuclei. The customized snakes are deformed to the exact shape of the nuclei. This allows for precise, automated analysis of nuclear size, shape and texture. Ten such features are computed for each nucleus, and the mean value, largest (or 'worst') value and standard error of each feature are found over the range of isolated cells. After 569 images were analyzed in this fashion, different combinations of features were tested to find those which best separate benign from malignant samples. Ten-fold cross-validation accuracy of 97\% was achieved using a single separating plane on three of the thirty features: mean texture, worst area and worst smoothness. This represents an improvement over the best diagnostic results in the medical literature. The system is currently in use at the University of Wisconsin Hospitals. The same feature set has also been utilized in the much more difficult task of predicting distant recurrence of malignancy in patients, resulting in an accuracy of 86\%.}
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}
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@misc{teamPandasdevPandasPandas2025,
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title = {Pandas-Dev/Pandas: {{Pandas}}},
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shorttitle = {Pandas-Dev/Pandas},
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author = {pandas development {team}, The},
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year = 2025,
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month = sep,
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doi = {10.5281/zenodo.17229934},
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urldate = {2025-10-16},
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abstract = {Pandas is a powerful data structures for data analysis, time series, and statistics.},
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howpublished = {Zenodo},
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keywords = {data science,python},
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file = {/home/lars/Zotero/storage/QV289HHN/17229934.html}
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}
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@article{Waskom2021,
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title = {Seaborn: Statistical Data Visualization},
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author = {Waskom, Michael L.},
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year = 2021,
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journal = {Journal of Open Source Software},
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volume = {6},
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number = {60},
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pages = {3021},
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publisher = {The Open Journal},
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doi = {10.21105/joss.03021}
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}
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@misc{williamwolbergBreastCancerWisconsin1993,
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title = {Breast {{Cancer Wisconsin}} ({{Diagnostic}})},
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author = {William Wolberg, Olvi Mangasarian},
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year = 1993,
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publisher = {UCI Machine Learning Repository},
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doi = {10.24432/C5DW2B},
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urldate = {2025-11-01}
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}
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