Nonparametric Learning in High Dimensions - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Learning in High Dimensions write by Han Liu. This book was released on 2010. Nonparametric Learning in High Dimensions available in PDF, EPUB and Kindle. Abstract: "This thesis develops flexible and principled nonparametric learning algorithms to explore, understand, and predict high dimensional and complex datasets. Such data appear frequently in modern scientific domains and lead to numerous important applications. For example, exploring high dimensional functional magnetic resonance imaging data helps us to better understand brain functionalities; inferring large-scale gene regulatory network is crucial for new drug design and development; detecting anomalies in high dimensional transaction databases is vital for corporate and government security. Our main results include a rigorous theoretical framework and efficient nonparametric learning algorithms that exploit hidden structures to overcome the curse of dimensionality when analyzing massive high dimensional datasets. These algorithms have strong theoretical guarantees and provide high dimensional nonparametric recipes for many important learning tasks, ranging from unsupervised exploratory data analysis to supervised predictive modeling. In this thesis, we address three aspects: 1 Understanding the statistical theories of high dimensional nonparametric inference, including risk, estimation, and model selection consistency; 2 Designing new methods for different data-analysis tasks, including regression classification, density estimation, graphical model learning, multi-task learning, spatial-temporal adaptive learning; 3 Demonstrating the usefulness of these methods in scientific applications, including functional genomics, cognitive neuroscience, and meteorology. In the last part of this thesis, we also present the future vision of high dimensional and large-scale nonparametric inference."
High-Dimensional Statistics
High-Dimensional Statistics - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook High-Dimensional Statistics write by Martin J. Wainwright. This book was released on 2019-02-21. High-Dimensional Statistics available in PDF, EPUB and Kindle. A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.
Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications
Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications write by . This book was released on 2006. Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications available in PDF, EPUB and Kindle. Nonparametric methods have become increasingly popular in statistics and probabilistic AI communities. One well-known nonparametric method is "nearest-neighbor" . It uses the observations in the training set T closest in input space to a query q to form the prediction of q. Specifically, when k of the observations in T are considered, it is called k-nearest-neighbor (or k-NN). Despite its simplicity, k-NN and its variants have been successful in many machine learning problems, including pattern recognition, text categorization, information retrieval, computational statistics, database and data mining. It is also used for estimating sample distributions and Bayes error. However, k-NN and many related nonparametric methods remain hampered by their computational complexity. Many spatial methods, such as metric-trees, have been proposed to alleviate the computational cost, but the effectiveness of these methods decreases as the number of dimensions of feature vectors increases. From another direction, researchers are trying to develop ways to find approximate answers. The premise of this research is that in many cases it is not necessary to insist on the exact answers; instead, determining an approximate answer should be sufficient. In fact, some approximate methods show good performance in a number of applications, and some methods enjoy very good theoretical soundness. However, when facing hundreds or thousands dimensions, many algorithms do not work well in reality.
Nonparametric Learning from Examples in Very High Dimensional Spaces
Nonparametric Learning from Examples in Very High Dimensional Spaces - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Nonparametric Learning from Examples in Very High Dimensional Spaces write by Gregory Zlatko Grudić. This book was released on 1997. Nonparametric Learning from Examples in Very High Dimensional Spaces available in PDF, EPUB and Kindle.
Statistics for High-Dimensional Data
Statistics for High-Dimensional Data - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Statistics for High-Dimensional Data write by Peter Bühlmann. This book was released on 2011-06-08. Statistics for High-Dimensional Data available in PDF, EPUB and Kindle. Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.
