標(biāo)題: Titlebook: Data Science and Predictive Analytics; Biomedical and Healt Ivo D. Dinov Textbook 2023Latest edition The Editor(s) (if applicable) and The [打印本頁] 作者: children 時間: 2025-3-21 18:29
書目名稱Data Science and Predictive Analytics影響因子(影響力)
書目名稱Data Science and Predictive Analytics影響因子(影響力)學(xué)科排名
書目名稱Data Science and Predictive Analytics網(wǎng)絡(luò)公開度
書目名稱Data Science and Predictive Analytics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data Science and Predictive Analytics被引頻次
書目名稱Data Science and Predictive Analytics被引頻次學(xué)科排名
書目名稱Data Science and Predictive Analytics年度引用
書目名稱Data Science and Predictive Analytics年度引用學(xué)科排名
書目名稱Data Science and Predictive Analytics讀者反饋
書目名稱Data Science and Predictive Analytics讀者反饋學(xué)科排名
作者: debunk 時間: 2025-3-21 23:06 作者: 不感興趣 時間: 2025-3-22 04:16 作者: arrhythmic 時間: 2025-3-22 07:25 作者: 智力高 時間: 2025-3-22 11:19
Textbook 2023Latest edition-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protoc作者: Libido 時間: 2025-3-22 13:14 作者: Libido 時間: 2025-3-22 18:47
Basic Visualization and Exploratory Data Analytics,erent data structures, measuring sample statistics for quantitative variables, plotting sample histograms and model distribution functions, and scraping data from websites. In addition, we will cover exploratory data analytical (EDA) techniques, handling of incomplete (missing) data, and cohort-rebalancing of imbalanced groups.作者: 最低點(diǎn) 時間: 2025-3-23 00:54
Linear Algebra, Matrix Computing, and Regression Modeling,ession modeling, ordinary least squares estimation, and other machine learning and artificial intelligence algorithms. These techniques will be demonstrated using simulated data, observed data of baseball players, and clinical data of heart attack patients.作者: Expertise 時間: 2025-3-23 03:44 作者: foodstuff 時間: 2025-3-23 08:37
Big Longitudinal Data Analysis,s analysis, autoregressive integrated moving average (ARIMA) models, structural equation models (SEM), linear mixed models, generalized estimating equations (GEE), recurrent neural networks (RNN), and long short-term memory (LSTM) networks.作者: 就職 時間: 2025-3-23 12:11 作者: inchoate 時間: 2025-3-23 15:42
Geotechnologies and the Environmentdentifying groups of collocated objects, and explicating interrelations between different objects in transactional data. We demonstrate these methods using text from course syllabi, job descriptions, film reviews, and head and neck cancer medication descriptions.作者: 搖曳 時間: 2025-3-23 18:41 作者: 血友病 時間: 2025-3-24 00:34
J. E. Gubernatis,R. N. Silver,M. Jarrell key feature of the R markdown electronic notebook. Uniquely, R provides support for direct and dynamic integration of objects, data, and functions across dozens of other programming languages into the same analytical protocol.作者: HALL 時間: 2025-3-24 06:25
S. Saito,A. Oshiyama,Y. Miyamotoell as modern alternative strategies for ranking variable importance, such as regularized linear modeling. In addition, we will present knockoff feature filtering as a controlled variable selection method with explicit control over the false discovery rate of salient features.作者: cogent 時間: 2025-3-24 07:26
,Qualitative Learning Methods—Text Mining, Natural Language Processing, and Apriori Association Ruledentifying groups of collocated objects, and explicating interrelations between different objects in transactional data. We demonstrate these methods using text from course syllabi, job descriptions, film reviews, and head and neck cancer medication descriptions.作者: 異常 時間: 2025-3-24 12:23 作者: Osteoarthritis 時間: 2025-3-24 15:52
Specialized Machine Learning Topics, key feature of the R markdown electronic notebook. Uniquely, R provides support for direct and dynamic integration of objects, data, and functions across dozens of other programming languages into the same analytical protocol.作者: declamation 時間: 2025-3-24 22:52
Variable Importance and Feature Selection,ell as modern alternative strategies for ranking variable importance, such as regularized linear modeling. In addition, we will present knockoff feature filtering as a controlled variable selection method with explicit control over the false discovery rate of salient features.作者: Ointment 時間: 2025-3-25 03:13 作者: 誘使 時間: 2025-3-25 06:11
https://doi.org/10.1007/978-3-642-17413-1 emphasize the importance of their ethical, responsible, and reproducible practical use. This chapter also covers the foundations of R, contrasts R against other languages and computational data science platforms, and introduces basic functions and data objects, formats, and simulation.作者: Infant 時間: 2025-3-25 09:29 作者: MORPH 時間: 2025-3-25 14:26 作者: Immunization 時間: 2025-3-25 18:54
Computational Approach to Riemann Surfacesniques, such as principal and independent component analyses, factor analysis, matrix singular value decomposition, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. These methods will be applied to hand-written digit recognition imaging data and Parkinson’s disease clinical data.作者: 表否定 時間: 2025-3-25 20:29
The Geometric Attributes of Paintingss analysis, autoregressive integrated moving average (ARIMA) models, structural equation models (SEM), linear mixed models, generalized estimating equations (GEE), recurrent neural networks (RNN), and long short-term memory (LSTM) networks.作者: detach 時間: 2025-3-26 02:28
Function Optimization,anual and automated function optimization. We demonstrate a number of examples including a generic healthcare manufacturer product optimization problem, data denoising, and optimization of specific multivariate test functions.作者: PALSY 時間: 2025-3-26 08:18 作者: cocoon 時間: 2025-3-26 09:04 作者: 機(jī)警 時間: 2025-3-26 15:44 作者: 發(fā)電機(jī) 時間: 2025-3-26 19:44 作者: 聽覺 時間: 2025-3-26 23:45 作者: 責(zé)問 時間: 2025-3-27 01:25
Geotechnologies and the Environmenttional data. Natural language processing and text mining techniques include automated algorithms for semantic mapping, information extraction, and understanding of human language. The main goal of text mining and natural language processing is to discover relevant contextual information that may be 作者: Free-Radical 時間: 2025-3-27 07:28 作者: 冷淡周邊 時間: 2025-3-27 09:31
https://doi.org/10.1007/978-3-319-11469-9ficial intelligence applications. This chapter presents various strategies for model validation and performance improvement. Specifically, we will discuss various performance metrics, internal statistical cross-validation, hyper-parameter tuning, and forecasting accuracy.作者: choroid 時間: 2025-3-27 16:27
J. E. Gubernatis,R. N. Silver,M. Jarrellary, and dynamic data formats. All data analytics are always predicated on efficient, consistent, and reliable data import. In this chapter, we will focus on examples of specialized types of datasets and database interfaces. We will discuss on-the-fly (online) streamed data processing, random data g作者: moratorium 時間: 2025-3-27 20:14
S. Saito,A. Oshiyama,Y. Miyamotos for unique solutions of modeling equations, and contextual difficulties with mechanistic understanding of any derived results. Feature selection and identification of variable importance represent common steps in many bioinformatics, healthcare, and biomedical data analytics protocols that aim to 作者: cauda-equina 時間: 2025-3-28 01:52
The Geometric Attributes of Paintingsgitudinal processes, and various evaluation criteria for assessing algorithmic performance. Specifically, we will cover the fundamentals of time-series analysis, autoregressive integrated moving average (ARIMA) models, structural equation models (SEM), linear mixed models, generalized estimating equ作者: 落葉劑 時間: 2025-3-28 04:04 作者: 無所不知 時間: 2025-3-28 08:43 作者: Lethargic 時間: 2025-3-28 12:32
Data Science and Predictive Analytics978-3-031-17483-4Series ISSN 2520-1298 Series E-ISSN 2520-1301 作者: Albinism 時間: 2025-3-28 17:53
https://doi.org/10.1007/978-3-319-11469-9arning methods. These techniques are applied to model market data, compute approximations of power functions, perform optical character recognition of handwritten text, and examine quality of life in chronic disease.作者: Oligarchy 時間: 2025-3-28 20:10
https://doi.org/10.1007/978-3-319-11469-9ficial intelligence applications. This chapter presents various strategies for model validation and performance improvement. Specifically, we will discuss various performance metrics, internal statistical cross-validation, hyper-parameter tuning, and forecasting accuracy.作者: Obituary 時間: 2025-3-28 23:26
Black Box Machine Learning Methods,arning methods. These techniques are applied to model market data, compute approximations of power functions, perform optical character recognition of handwritten text, and examine quality of life in chronic disease.作者: Virtues 時間: 2025-3-29 06:56
Model Performance Assessment, Validation, and Improvement,ficial intelligence applications. This chapter presents various strategies for model validation and performance improvement. Specifically, we will discuss various performance metrics, internal statistical cross-validation, hyper-parameter tuning, and forecasting accuracy.作者: MOT 時間: 2025-3-29 09:18 作者: 傳染 時間: 2025-3-29 14:17
The Springer Series in Applied Machine Learninghttp://image.papertrans.cn/d/image/263103.jpg作者: FLEET 時間: 2025-3-29 17:32 作者: Integrate 時間: 2025-3-29 20:02
978-3-031-17485-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 圣歌 時間: 2025-3-30 03:34 作者: 丑惡 時間: 2025-3-30 07:02
Supervised Classification,Chapter 5 covers supervised k-nearest neighbors’ classification, na?ve Bayes probabilistic learning, and divide and conquer decision tree classification methods. These techniques are applied for predicting galaxy spins, modeling head and neck cancer medication, and understanding quality of life and chronic disease.作者: addition 時間: 2025-3-30 11:47 作者: 離開真充足 時間: 2025-3-30 13:22 作者: hidebound 時間: 2025-3-30 19:11 作者: 細(xì)菌等 時間: 2025-3-30 21:43
Linear and Nonlinear Dimensionality Reduction,corresponding simple lower-dimensional fundamental constituents. Specifically, we will discuss linear and nonlinear data dimensionality reduction techniques, such as principal and independent component analyses, factor analysis, matrix singular value decomposition, t-distributed stochastic neighbor 作者: 思考才皺眉 時間: 2025-3-31 02:25
Black Box Machine Learning Methods,arning methods. These techniques are applied to model market data, compute approximations of power functions, perform optical character recognition of handwritten text, and examine quality of life in chronic disease.
