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Discussion: The Dantzig selector: Statistical estimation when $p$ is much larger than $n$ [PDF]
Annals of Statistics 2007, Vol. 35, No. 6, 2352-2357, 2008 Discussion of "The Dantzig selector: Statistical estimation when $p$ is much larger than $n$" [math/0506081]
arxiv +5 more sources
Technometrics, 2021 A machine learning system, in general, learns from the environment, but statistical machine learning programs (systems) learn from the data. This chapter presents techniques for statistical machine learning using Support Vector Machines (SVM) to ...
Yuhai Wu
semanticscholar +1 more source
Manual for Theoretical Chemistry, 2021 Professor Chen Ning Yang has made seminal and influential contributions in many different areas in theoretical physics. This talk focuses on his contributions in statistical mechanics, a field in which Professor Yang has held a continual interest for ...
Uwe-Jens Wiese, Albert Einstein
semanticscholar +1 more source
Clustering CITE-seq data with a canonical correlation-based deep learning method
Frontiers in Genetics, 2022 Single-cell multiomics sequencing techniques have rapidly developed in the past few years. Among these techniques, single-cell cellular indexing of transcriptomes and epitopes (CITE-seq) allows simultaneous quantification of gene expression and surface ...
Musu Yuan +4 more
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Statistical analysis of vegetable productivity dynamics of Uzbekistan [PDF]
BIO Web of Conferences, 2023 In all fields, there are a lot of random events at a given time. In particular, the process of growing agricultural crops, which is repeated over a certain period, that is, seasonally, is the basis for our analysis as a discrete {Yt,t ∈ T} random dynamic
Fayziev Akhtamjon +3 more
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Seaborn: Statistical Data Visualization
Journal of Open Source Software, 2021 seaborn is a library for making statistical graphics in Python. It provides a high-level interface to matplotlib and integrates closely with pandas data structures. Functions in the seaborn library expose a declarative, dataset-oriented API that makes it
Michael L. Waskom
semanticscholar +1 more source
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation [PDF]
Conference on Empirical Methods in Natural Language Processing, 2014 In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the ...
Kyunghyun Cho +6 more
semanticscholar +1 more source
An introduction to statistical learning with applications in R
Statistical Theory and Related Fields, 2021 The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Fariha Sohil +2 more
semanticscholar +1 more source
Frontiers in Psychiatry, 2022 BackgroundIdentifying patients at a high risk of psychosis relapse is crucial for early interventions. A relevant psychiatric clinical context is often recorded in clinical notes; however, the utilization of unstructured data remains limited.
Dong Yun Lee +9 more
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Development and Validation of Smoking Abstinence Self-efficacy Scale among Iranian Female Adolescents [PDF]
Journal of Evidence-Based Care, 2020 Background: Among different factors, self-efficacy is one of the most suitable components for predicting smoking behavior among adolescents. Therefore, it is essential to design a native tool to measure this high-risk behavior in adolescents and ...
Zienab Rafiee +3 more
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