標題: Titlebook: Machine Learning Models and Algorithms for Big Data Classification; Thinking with Exampl Shan Suthaharan Book 2016 Springer Science+Busines [打印本頁] 作者: 變成小松鼠 時間: 2025-3-21 18:12
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作者: Nonthreatening 時間: 2025-3-21 20:46
Shan Suthaharanone can achieve by placing Leibniz‘s philosophy in the context of the sources for two of the most basic concerns of his philosophical career: his metaphysics of individuals and the principle oftheir individuation. In this book I provide for the first time a detailed examination of these two Leibnizi作者: engrossed 時間: 2025-3-22 04:05 作者: Embolic-Stroke 時間: 2025-3-22 06:44 作者: uveitis 時間: 2025-3-22 11:36 作者: 出來 時間: 2025-3-22 15:16
ticular, it focuses on his theory of parallel lines and his attempts to prove the famous Parallel Postulate. Furthermore it explains the role that Leibniz’s work played in the development of non-Euclidean geometry. The first part is an overview of his epistemology of geometry and a few of his geomet作者: 沙草紙 時間: 2025-3-22 18:46
Shan Suthaharanally relevant. In Leibniz’ times, the text of Euclid’s Elements still represented the starting point for any advanced mathematical theory, including Leibniz’ most celebrated discovery, the Calculus. The Greek treatise, on the other hand, was also the main model for deductive reasoning, and the touch作者: Galactogogue 時間: 2025-3-22 23:06
Shan Suthaharanphie als streng beweisbare Fachwissenschaft sieht darin den auszumerzenden Erdenrest, der entweder blo?e individuelle Eigenheit oder zeitgebundenes Schicksal ist, nach dessen Abzug das Ewige und Wertvolle zurückbleibt. Gleichwohl werden wir uns damit abfinden müssen, da? Leibnizens Lehre nicht als G作者: intercede 時間: 2025-3-23 05:03 作者: antedate 時間: 2025-3-23 08:15
Shan Suthaharan Hat doch auch Goethe die sch?pferische Kraft als ein D?monisches, in dem G?ttliches und Teuflisches zusammenwirken, gedeutet. Aber es ist nicht Aufgabe der Philosophie, unl?sbare Probleme zu entscheiden, sondern unsere Organe für die Erkenntnis des sch?pferischen und des zerst?renden Geschehens auf作者: 創(chuàng)新 時間: 2025-3-23 09:42 作者: 不感興趣 時間: 2025-3-23 17:06 作者: insomnia 時間: 2025-3-23 18:41
Shan Suthaharanl der letzte Universalgelehrte, der auf allen wesentlichen Wissensgebieten seiner Zeit originelle und innovative Leistungen erbracht hat: als reformorientierter Jurist, multilateral denkender Diplomat, als Mathematiker der Infinitesimalrechnung, als Erfinder einer Rechenmaschine und im Bergbau der H作者: obstinate 時間: 2025-3-24 00:05
Shan Suthaharanl der letzte Universalgelehrte, der auf allen wesentlichen Wissensgebieten seiner Zeit originelle und innovative Leistungen erbracht hat: als reformorientierter Jurist, multilateral denkender Diplomat, als Mathematiker der Infinitesimalrechnung, als Erfinder einer Rechenmaschine und im Bergbau der H作者: 內(nèi)行 時間: 2025-3-24 02:29
ied Wilhelm Leibniz (1646-1716) war ein Universalgenie, und ihm gelangen bahnbrechende Leistungen in fast allen Gebieten der Wissenschaft, insbesondere in der Philosophie (Relativit?t von Raum und Zeit), der Mathematik (Infinitesimalrechnung, Determinantentheorie, bin?res Zahlsystem, Konstruktion ei作者: 勾引 時間: 2025-3-24 09:23
Shan Suthaharanied Wilhelm Leibniz (1646-1716) war ein Universalgenie, und ihm gelangen bahnbrechende Leistungen in fast allen Gebieten der Wissenschaft, insbesondere in der Philosophie (Relativit?t von Raum und Zeit), der Mathematik (Infinitesimalrechnung, Determinantentheorie, bin?res Zahlsystem, Konstruktion ei作者: 逢迎春日 時間: 2025-3-24 14:10
Shan Suthaharanuch Entsprechungen zwischen einem Gedankengang bei Leibniz und in neuhumanistischen Entwürfen festgestellt wurden.. Auch liegen für einzelne gro?e Gestalten des deutschen Humanismus Arbeiten über die Korrespondenz ihrer Grundlehren und der Leibnizschen Philosophie vor.. Es fehlt hingegen bislang ein作者: 禁止 時間: 2025-3-24 14:54 作者: AROMA 時間: 2025-3-24 20:28
Shan Suthaharaninterest of Leibniz’s contributions in this area will be a theme to which we repeatedly return. My aim in this chapter is rather different. Though Leibniz may have occasionally possessed insights that were, as Reichenbach put it, “too sophisticated” by the measures of his intellectual context, it is作者: progestogen 時間: 2025-3-24 23:51
Leibniz, whose writings brim with speculations about infinite temporal regressions, the unity of time, and the relation between time and causation. Even so, this facet of his thought has been almost entirely neglected by commentators, a neglect that has been to the detriment of both Leibniz scholars作者: 受辱 時間: 2025-3-25 03:57
Science of Information,verview focuses on two important paradigms: (1) big data paradigm, which describes a problem space for the big data analytics, and (2) machine learning paradigm, which describes a solution space for the big data analytics. It also includes a preliminary description of the important elements of data 作者: Apogee 時間: 2025-3-25 11:09 作者: entreat 時間: 2025-3-25 13:33
Big Data Analytics objective of this chapter is to illustrate some of the meaningful changes that may occur in a set of data when it is transformed into big data through evolution. To make this objective practical and interesting, a split-merge-split frameworkis developed, presented, and applied in this chapter. A se作者: rods366 時間: 2025-3-25 19:03
Distributed File Systemfication problem. This system can help one to implement, test, and evaluate various machine-learning techniques presented in this book for learning purposes. The objectives include a detailed explanation of the Hadoop framework and the Hadoop system, the presentation of the Internet resources that c作者: 使聲音降低 時間: 2025-3-25 20:49
MapReduce Programming Platformlies on its underlying structures, the parametrization, and the parallelization. These structures have been explained clearly in this chapter. The implementation of these structures requires a MapReduce programming platform. An explanation of this programming platform is also presented together with作者: 信任 時間: 2025-3-26 00:08
Modeling and Algorithmsd supervised learning (regression and classification) and unsupervised learning (clustering) using examples. Modeling and algorithms will be explained based on the domain division characteristics, batch learning and online learning will be explained based on the availability of the data domain, and 作者: 共同生活 時間: 2025-3-26 08:16
Supervised Learning Modelsping that projects a data domain into a response set, and thus helps extract knowledge (known) from data (unknown). These learning models, in simple form, can be grouped into predictive models and classification models. Firstly, the predictive models, such as the standard regression, ridge regressio作者: Outwit 時間: 2025-3-26 12:21
Supervised Learning Algorithmsthe supervised learning algorithms support the search for optimal values for the model parameters by using large data sets without overfitting the model. Therefore, a careful design of the learning algorithms with systematic approaches is essential. The machine learning field suggests three phases f作者: pessimism 時間: 2025-3-26 15:45
Support Vector Machinecan help the multidomain applications in a big data environment. However, the support vector machine is mathematically complex and computationally expensive. The main objective of this chapter is to simplify this approach using process diagrams and data flow diagrams to help readers understand theor作者: fiction 時間: 2025-3-26 20:38
Decision Tree Learning tree. It has two categories: classification tree and regression tree. The theory and applications of these decision trees are explained in this chapter. These techniques require tree split algorithms to build the decision trees and require quantitative measures to build an efficient tree via traini作者: 合適 時間: 2025-3-27 00:44 作者: Project 時間: 2025-3-27 01:12
Deep Learning Modelsnd provide programming examples that help you clearly understand these approaches. These techniques heavily depend on the stochastic gradient descent approach; and this approach is also discussed in detail with simple iterative examples. These parametrized deep learning techniques are also dependent作者: addict 時間: 2025-3-27 07:38
Chandelier Decision Tree tree and the random forest. The chapter also presents a previously proposed algorithm called the unit circle algorithm (UCA) and proposes a family of UCA-based algorithms called the unit circle machine (UCM), unit ring algorithm (URA), and unit ring machine (URM). The unit circle algorithm integrat作者: d-limonene 時間: 2025-3-27 12:50
Dimensionality Reductionis, that can support scaling-up machine learning. The standard and flagged feature hashing approaches are explained in detail. The feature hashing approach suffers from the hash collision problem, and this problem is reported and discussed in detail in this chapter, too. Two collision controllers, f作者: 歸功于 時間: 2025-3-27 17:30 作者: 放逐某人 時間: 2025-3-27 17:57
MapReduce Programming Platformprovide good programming practices to the users of the MapReduce programming platform in the context of big data processing and analysis. Several programming examples are also presented to help the reader to practice coding principles and better understand the MapReduce framework.作者: offense 時間: 2025-3-28 00:21
Random Forest Learning chapter include detailed discussions on these approaches. The chapter also discusses the training and testing algorithms that are suitable for the random forest supervised learning. The chapter also presents simple examples and visual aids to better understand the random forest supervised learning technique.作者: 使腐爛 時間: 2025-3-28 05:35 作者: 打火石 時間: 2025-3-28 09:37
1571-0270 overcome Big Data classification problems that industries, .This book presents machine learning models and algorithms to address big data classification problems. Existing machine learning techniques like the decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach)作者: 得體 時間: 2025-3-28 13:07 作者: 懶惰民族 時間: 2025-3-28 17:40
Distributed File Systemstep-by-step instruction to build the RevolutionAnalytics’ RHadoop system for your big data computing environment. The objective also includes the presentation of simple examples to test the system to ensure the Hadoop system works. A brief discussion on setting up a multi node Hadoop system is also presented.作者: circumvent 時間: 2025-3-28 21:43 作者: HEED 時間: 2025-3-29 00:53
Decision Tree Learninghe training algorithms suitable for classification tree and regression tree models. Simple examples and visual aids explain the difficult concepts so that readers can easily grasp the theory and applications of decision tree.作者: 細微的差異 時間: 2025-3-29 06:23
Dimensionality Reductionf eigenvalues and eigenvectors, and these terminologies are explained in detail with examples. The principal component analysis is also explained using a simple two-dimensional example, and several coding examples are also presented.作者: offense 時間: 2025-3-29 09:15
Book 2016decision tree (a hierarchical approach), random forest (an ensemble hierarchical approach), and deep learning (a layered approach) are highly suitable for the system that can handle such problems. This book helps readers, especially students and newcomers to the field of big data and machine learnin作者: 要素 時間: 2025-3-29 13:16 作者: 兇殘 時間: 2025-3-29 17:29 作者: 邊緣帶來墨水 時間: 2025-3-29 23:22 作者: peritonitis 時間: 2025-3-30 02:54 作者: Fabric 時間: 2025-3-30 05:32
Shan SuthaharanAddresses a new and hot field of Big Data Science and Engineering.Offers new Machine Learning techniques and solutions.Provides solutions to overcome Big Data classification problems that industries, 作者: 禁令 時間: 2025-3-30 10:06
Integrated Series in Information Systemshttp://image.papertrans.cn/m/image/620410.jpg作者: 感情脆弱 時間: 2025-3-30 14:16
https://doi.org/10.1007/978-1-4899-7641-3Big Data; Classification; Data Visualization; Machine Learning; Supervised Learning; Unit Circle Machine作者: 輕彈 時間: 2025-3-30 18:03
Shan Suthaharanongenial to twentieth century philosophical methodologies, especially those that have enjoyed some prominence in recent Anglo-American philosophy. Moreover, as we shall see, Leibniz is not a modem philosopher, when ‘modem‘ is understood to mean making a sharp break with medieval philosophy. Indeed,
