標題: Titlebook: Machine Learning Foundations; Supervised, Unsuperv Taeho Jo Book 2021 Springer Nature Switzerland AG 2021 Machine Learning.Supervised Learn [打印本頁] 作者: Amalgam 時間: 2025-3-21 17:17
書目名稱Machine Learning Foundations影響因子(影響力)
書目名稱Machine Learning Foundations影響因子(影響力)學科排名
書目名稱Machine Learning Foundations網(wǎng)絡公開度
書目名稱Machine Learning Foundations網(wǎng)絡公開度學科排名
書目名稱Machine Learning Foundations被引頻次
書目名稱Machine Learning Foundations被引頻次學科排名
書目名稱Machine Learning Foundations年度引用
書目名稱Machine Learning Foundations年度引用學科排名
書目名稱Machine Learning Foundations讀者反饋
書目名稱Machine Learning Foundations讀者反饋學科排名
作者: Rankle 時間: 2025-3-21 21:07
Taeho Jond student learning. Teacher knowledge cannot be separated from the subject matter being investigated, from how that subject matter can be represented for learners, from what we know about students’ thinking in specific domains, or from teacher beliefs (Fennema & Franke, 1992, S. 161).作者: 避開 時間: 2025-3-22 03:26 作者: nonradioactive 時間: 2025-3-22 04:50 作者: 無法破譯 時間: 2025-3-22 11:47
Taeho Jond student learning. Teacher knowledge cannot be separated from the subject matter being investigated, from how that subject matter can be represented for learners, from what we know about students’ thinking in specific domains, or from teacher beliefs (Fennema & Franke, 1992, S. 161).作者: Ferritin 時間: 2025-3-22 14:23
Taeho Jo Agens dieser Krankheiten ist lediglich bekannt, da? es die gew?hnlichen, bakteriendichten Filter passiert und unsichtbar ist. Der am besten zum Ziele führende Weg, die Gegenwart solcher Infektionserreger zu ermitteln, ist das Studium der durch sie in ihren Wirtstieren verursachten Gewebsver?nderung作者: Promotion 時間: 2025-3-22 20:54
rn?hrter Zellen an Volumen abnehmen. Die Kerne zeigen h?ufig das Bild der Schrumpfung und Verklumpung des Chromatins (Pyknosis). Zellstoffwechselprodukte, wie Fett oder Glykogen k?nnen aufgebraucht und verschwunden sein. Auf der anderen Seite k?nnen bei der Atrophie Stoffwechselprodukte (Fette, Lipo作者: delusion 時間: 2025-3-23 00:10 作者: NEXUS 時間: 2025-3-23 04:35 作者: OUTRE 時間: 2025-3-23 05:35 作者: Intuitive 時間: 2025-3-23 11:47 作者: 微塵 時間: 2025-3-23 17:20
Numerical Vectors the summarization on this chapter and the further discussions. This chapter is intended to characterize mathematically vectors and matrices as the foundation for understanding machine learning algorithms.作者: upstart 時間: 2025-3-23 18:28 作者: 評論者 時間: 2025-3-23 23:21 作者: 閑蕩 時間: 2025-3-24 03:09
Instance Based LearningNN. We also present the modified versions of KNN as its variants. In this chapter, we assume that the KNN is the supervised learning algorithm, but we cover its unsupervised version in the next part..In Sect. 5.1, we provide the overview of the instance based learning, and in Sect. 5.2, we mention t作者: 航海太平洋 時間: 2025-3-24 07:25
Probabilistic Learningo provide the background for understanding the chapter. We describe in detail some probabilistic classifiers such as Bayes Classifier and Naive Bayes as the popular and simple machine learning algorithms. We cover the Bayesian Learning as the more advanced learning methods than the two probabilistic作者: 神經(jīng) 時間: 2025-3-24 14:26
Decision Treemples, entirely. In Sect. 7.4, we mention some variants of the decision tree, and in Sect. 7.5, we make the summarization on this chapter and the further discussions. This chapter is intended to describe the classification process, the learning process, and the variants of the decision tree.作者: TIGER 時間: 2025-3-24 15:57 作者: 小隔間 時間: 2025-3-24 19:51
K Means Algorithme k means algorithm. In Sect. 10.4, we mention the variants of the k means algorithm, and in Sect. 10.5, we make the summarization on this chapter and the further discussions. This chapter is intended to describe the clustering process and the variants of the k means algorithm.作者: tendinitis 時間: 2025-3-25 00:59 作者: DNR215 時間: 2025-3-25 05:21
Advanced Clusteringention the clustering governance as the management and the maintenance of existing clusters and, in Sect. 12.5, make the summarization on this chapter and the further discussions. This chapter covers the clustering evaluation metric, the clustering algorithms with their parameter turning, and the cl作者: 時間等 時間: 2025-3-25 10:01 作者: 兇殘 時間: 2025-3-25 12:01 作者: 值得贊賞 時間: 2025-3-25 16:49
Temporal Learningrobabilistic model, called discrete Markov model. In Sect. 15.3, we describe the Hidden Markov Model as the temporal learning algorithm with the three problems. In Sect. 15.4, we mention the application of the Hidden Markov Model to the text topic analysis, and in Sect. 15.5, we make the summarizati作者: 闖入 時間: 2025-3-25 21:46 作者: Increment 時間: 2025-3-26 01:51
Support Vector Machinen Sect. 8.4 and make the summarization on this chapter and the further discussions in Sect. 8.5. This chapter is intended to describe the SVM entirely with respect to the classification process, the learning process, and the variants.作者: 致敬 時間: 2025-3-26 07:55 作者: Mechanics 時間: 2025-3-26 08:35 作者: Obvious 時間: 2025-3-26 15:22
Introductione able to apply the machine learning algorithms in the functional view. We describe briefly the four types of machine learning algorithms: the supervised learning, the unsupervised one, the reinforced one, and the semi-supervised one. We explore other areas which are related with the current area, c作者: 有偏見 時間: 2025-3-26 19:45 作者: 蛙鳴聲 時間: 2025-3-27 00:09
Data Encodingsts of records is the typical raw data type, and it is relatively easy to encode them into numerical vectors. We mention the textual data as the most popular raw data type in the real world and study the process of indexing a text into a list of words and encoding it into a numerical vector. We also作者: nauseate 時間: 2025-3-27 04:23
Simple Machine Learning Algorithms, it is assumed that the given task is a binary classification, and the regression or the multiple classification may be decomposed into binary classifications. Some simple machine learning algorithms, which are given as threshold rules or hypercubes, will be mentioned for helping understanding the 作者: Seizure 時間: 2025-3-27 06:36 作者: 同位素 時間: 2025-3-27 10:31
Probabilistic Learningobability theory, which is called Bayes Theorem, in order to provide the background for understanding the chapter. We describe in detail some probabilistic classifiers such as Bayes Classifier and Naive Bayes as the popular and simple machine learning algorithms. We cover the Bayesian Learning as th作者: Anhydrous 時間: 2025-3-27 17:26 作者: theta-waves 時間: 2025-3-27 20:36
Support Vector Machiner classifier and the basis for deriving the SVM. In the main part, we cover the classification process, the constraints, and the learning process of the SVM. We survey some variants of the SVM that are expansions of the standard SVM. The SVM is applicable to a nonlinear classification problem, robus作者: Lament 時間: 2025-3-28 00:30 作者: AWE 時間: 2025-3-28 06:02
K Means Algorithme KNN algorithm. With respect to the clustering process, we study the two main versions of the k means algorithm: the crisp k means algorithm and the fuzzy k means algorithm. The k medoid algorithm is mentioned as a variant of the k means algorithm, and the strategies of selecting representative ite作者: BLANK 時間: 2025-3-28 10:00 作者: CLASP 時間: 2025-3-28 13:45 作者: OTHER 時間: 2025-3-28 16:18 作者: Urologist 時間: 2025-3-28 19:01
Semi-supervised Learningen Networks: the unsupervised version, the supervised version, and the semi-supervised version. Afterward, as another kind of semi-supervised learning algorithm, we present some models with the combinations of the supervised learning algorithm with the unsupervised learning algorithm. We explore som作者: artless 時間: 2025-3-29 01:28
Temporal Learninground for understanding the Hidden Markov Model. In the main section, we describe the Hidden Markov Model that does the three cases: computing the probability of a state sequence, generating state sequence given an observation sequence, and estimating its parameters from a training set. We mention t作者: 承認 時間: 2025-3-29 06:55 作者: expository 時間: 2025-3-29 07:58 作者: 積極詞匯 時間: 2025-3-29 13:00
Taeho Johe complexity and dynamic nature of knowledge has become recognized. Researching teacher knowledge means more than investigating the number of mathematics courses teachers have taken or the procedural knowledge of mathematics they pocess. Knowledge of mathematics teaching includes knowledge of pedag作者: 輕打 時間: 2025-3-29 16:12
Taeho Johe complexity and dynamic nature of knowledge has become recognized. Researching teacher knowledge means more than investigating the number of mathematics courses teachers have taken or the procedural knowledge of mathematics they pocess. Knowledge of mathematics teaching includes knowledge of pedag作者: 粉筆 時間: 2025-3-29 20:31
Taeho Johe complexity and dynamic nature of knowledge has become recognized. Researching teacher knowledge means more than investigating the number of mathematics courses teachers have taken or the procedural knowledge of mathematics they pocess. Knowledge of mathematics teaching includes knowledge of pedag作者: 不要嚴酷 時間: 2025-3-30 02:58 作者: Rct393 時間: 2025-3-30 08:01
rkleinerung der einzelnen Zellelemente, ohne da? morphologische und chemische Ver?nderungen zugegen sein brauchen (einfache Atrophie), oder auf zahlenm??iger Verringerung der Zellen durch massenhaftes Zugrundegehen solcher (numerische Atrophie). Beide Vorg?nge laufen oft nebeneinander her. Die Folge作者: 全神貫注于 時間: 2025-3-30 10:28 作者: famine 時間: 2025-3-30 16:22 作者: 悲觀 時間: 2025-3-30 17:54 作者: 付出 時間: 2025-3-30 22:38 作者: 惡臭 時間: 2025-3-31 03:41 作者: 不溶解 時間: 2025-3-31 05:18 作者: Nonporous 時間: 2025-3-31 12:36
10樓作者: extinct 時間: 2025-3-31 13:38
10樓作者: consent 時間: 2025-3-31 21:17
10樓
