標題: Titlebook: An Introduction to Machine Learning; Miroslav Kubat Textbook 2021Latest edition Springer Nature Switzerland AG 2021 Bayesian classifiers.b [打印本頁] 作者: polysomnography 時間: 2025-3-21 18:32
書目名稱An Introduction to Machine Learning影響因子(影響力)
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書目名稱An Introduction to Machine Learning網(wǎng)絡(luò)公開度
書目名稱An Introduction to Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱An Introduction to Machine Learning被引頻次
書目名稱An Introduction to Machine Learning被引頻次學(xué)科排名
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書目名稱An Introduction to Machine Learning讀者反饋
書目名稱An Introduction to Machine Learning讀者反饋學(xué)科排名
作者: PSA-velocity 時間: 2025-3-21 22:39
Similarities: Nearest-Neighbor Classifiers,rom the same disease. Similar objects often belong to the same class—an observation underlying another popular approach to classification: when asked to determine the class of object ., find the training example most similar to it, and then label . with this similar example’s class.作者: Pert敏捷 時間: 2025-3-22 01:12
Inter-Class Boundaries: Linear and Polynomial Classifiers,n and negative examples in another. This motivates yet another machine-learning approach to classification: instead of the probabilities and similarities from the previous two chapters, the idea is to define a . that separates the two classes. This surface can be linear—and indeed, linear functions 作者: 協(xié)迫 時間: 2025-3-22 07:48
Decision Trees,e attribute vector that describes the example. In some applications, this scenario is unrealistic. A physician looking for the cause of her patient’s ailment may have nothing to begin with save a few subjective symptoms. To narrow the field of possible diagnoses, she prescribes a few lab tests, and,作者: investigate 時間: 2025-3-22 10:07
Artificial Neural Networks,e links that interconnect the neurons. The task for machine learning is to provide algorithms capable of finding weights that result in good classification behavior. This search is accomplished by a process commonly referred to as a neural network’s ..作者: 指派 時間: 2025-3-22 16:21
Computational Learning Theory,at it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking often disappoints the user. And so, even though this textbook does not want to be mathematical, it cannot help discussing at least the basic concepts of the ..作者: municipality 時間: 2025-3-22 20:33
Experience from Historical Applications,s has a way of complicating things, frustrating the engineer with unexpected hurdles, and challenging everybody’s notion of what exactly the induced classifier is supposed to do and why. Just as everywhere in the world of technology, a healthy dose of creativity is indispensable.作者: 里程碑 時間: 2025-3-23 00:32
Voting Assemblies and Boosting,exchanging diverse points of view that complement each other in ways likely to inspire unexpected solutions. Something similar can be done in machine learning, as well. A group of classifiers is created, each of them somewhat different. When they vote about a class label, their “collective wisdom” o作者: CYT 時間: 2025-3-23 02:32
Classifiers in the Form of Rule-Sets,fied by the .-part. The advantage is that the rule captures the logic underlying the given class and thus facilitates an explanation of why an example is to be labeled in this or that concrete way. Typically, a classifier of this kind is represented not by a single rule, but by a set of rules, a .. 作者: 種屬關(guān)系 時間: 2025-3-23 07:06 作者: LATER 時間: 2025-3-23 10:46
Statistical Significance,mes up . eight times out of ten, any sensible person will say this is nothing but a fluke, easily refuted by new trials. Similar caution is in place when evaluating a classifier in machine learning. To measure its performance on a testing set is not enough; just as important is an estimate of the ch作者: 使痛苦 時間: 2025-3-23 15:12
Induction in Multi-label Domains,he case. Quite often, an example is known to belong to two or more classes at the same time, sometimes to . classes. This situation presents certain new problems whose nature the engineer needs to understand.作者: 埋伏 時間: 2025-3-23 20:36
Deep Learning, fail. For another, the excessive detail of available attributes may obscure vital information about the data. To cope with these complications, more advanced techniques are needed. This is why . was born.作者: 嫻熟 時間: 2025-3-24 00:43
Reinforcement Learning: ,-Armed Bandits and Episodes,ery practical. In this way, the computer can learn how to navigate a complicated maze, how to balance a broom-stick, and even how to drive a car or how to play complicated games such as chess or Go. The principle is to “l(fā)earn from experience.” Facing diverse situations, the agent experiments, acts, 作者: Processes 時間: 2025-3-24 04:31 作者: 衰老 時間: 2025-3-24 06:59
https://doi.org/10.1007/978-3-030-81935-4Bayesian classifiers; boosting; computational learning theory; decision trees; genetic algorithms; linear作者: 協(xié)定 時間: 2025-3-24 10:46
Springer Nature Switzerland AG 2021作者: cauda-equina 時間: 2025-3-24 17:32 作者: JUST 時間: 2025-3-24 19:26
http://image.papertrans.cn/a/image/155326.jpg作者: FRAX-tool 時間: 2025-3-25 00:12
Von Lebensgemeinschaften?zum Metaorganismusew of her pictures, he will immediately see the tell-tale traits. As they say, a picture—an example—is worth a thousand words. Likewise, you will not become a professional juggler by just being told how to do it. The best any instructor can do is to offer some initial advice, and then let you practi作者: 極少 時間: 2025-3-25 07:22 作者: STANT 時間: 2025-3-25 11:14
https://doi.org/10.1007/978-3-662-65083-7n and negative examples in another. This motivates yet another machine-learning approach to classification: instead of the probabilities and similarities from the previous two chapters, the idea is to define a . that separates the two classes. This surface can be linear—and indeed, linear functions 作者: 變化 時間: 2025-3-25 14:32 作者: 時代 時間: 2025-3-25 17:43
https://doi.org/10.1007/978-3-642-94166-5e links that interconnect the neurons. The task for machine learning is to provide algorithms capable of finding weights that result in good classification behavior. This search is accomplished by a process commonly referred to as a neural network’s ..作者: medium 時間: 2025-3-25 22:39
Rudolf Demel,Otto Hoche,Paul Moritschat it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking often disappoints the user. And so, even though this textbook does not want to be mathematical, it cannot help discussing at least the basic concepts of the ..作者: epicardium 時間: 2025-3-26 03:40 作者: 木質(zhì) 時間: 2025-3-26 06:19
https://doi.org/10.1007/978-3-7091-2188-7exchanging diverse points of view that complement each other in ways likely to inspire unexpected solutions. Something similar can be done in machine learning, as well. A group of classifiers is created, each of them somewhat different. When they vote about a class label, their “collective wisdom” o作者: chronicle 時間: 2025-3-26 09:35 作者: troponins 時間: 2025-3-26 15:29
https://doi.org/10.1007/978-3-322-87998-1n class labels and then calculating the classifier’s error rate on these examples. In reality, however, error rate rarely paints the whole picture, and there are situations in which it is outright misleading. The reader needs to be acquainted with performance criteria that offer a more plastic view 作者: 矛盾心理 時間: 2025-3-26 18:57 作者: Statins 時間: 2025-3-27 00:07 作者: Ostrich 時間: 2025-3-27 01:52
Au?enbeziehungen der Universit?t fail. For another, the excessive detail of available attributes may obscure vital information about the data. To cope with these complications, more advanced techniques are needed. This is why . was born.作者: 一起 時間: 2025-3-27 08:21 作者: minaret 時間: 2025-3-27 13:30 作者: 蕁麻 時間: 2025-3-27 15:41 作者: Admire 時間: 2025-3-27 20:53
Artificial Neural Networks,e links that interconnect the neurons. The task for machine learning is to provide algorithms capable of finding weights that result in good classification behavior. This search is accomplished by a process commonly referred to as a neural network’s ..作者: 殺蟲劑 時間: 2025-3-28 01:53 作者: Brain-Waves 時間: 2025-3-28 03:50 作者: SHOCK 時間: 2025-3-28 08:03
Performance Evaluation,n class labels and then calculating the classifier’s error rate on these examples. In reality, however, error rate rarely paints the whole picture, and there are situations in which it is outright misleading. The reader needs to be acquainted with performance criteria that offer a more plastic view of the classifier’s behavior.作者: 向外 時間: 2025-3-28 10:29 作者: LAST 時間: 2025-3-28 14:45
Deep Learning, fail. For another, the excessive detail of available attributes may obscure vital information about the data. To cope with these complications, more advanced techniques are needed. This is why . was born.作者: inflate 時間: 2025-3-28 21:53 作者: 弄皺 時間: 2025-3-28 23:09
https://doi.org/10.1007/978-3-322-87998-1To facilitate the presentation of machine-learning techniques, this book has so far neglected certain practical issues that are non-essential for beginners but cannot be neglected in realistic applications. Now that the elementary principles have been explained, time has come to venture beyond the basics.作者: 開頭 時間: 2025-3-29 03:07
Au?enbeziehungen der Universit?t. seeks to obtain information from training sets in which the examples are not labeled with classes. This contrasts with the more traditional . that induces classifiers from pre-classified data.作者: FEAS 時間: 2025-3-29 09:11
https://doi.org/10.1007/978-3-642-83072-3The last chapter introduces the basic principles of reinforcement learning in its episodic formulation. Episodes, however, are of limited value in many realistic domains; in others, they cannot be used at all. This is why we often prefer the much more flexible approach built around the idea of . and immediate rewards.作者: Compassionate 時間: 2025-3-29 11:55 作者: Congestion 時間: 2025-3-29 15:50
Practical Issues to Know About,To facilitate the presentation of machine-learning techniques, this book has so far neglected certain practical issues that are non-essential for beginners but cannot be neglected in realistic applications. Now that the elementary principles have been explained, time has come to venture beyond the basics.作者: 滑稽 時間: 2025-3-29 21:16 作者: ACRID 時間: 2025-3-30 02:14
Reinforcement Learning: From TD(0) to Deep-Q-Learning,The last chapter introduces the basic principles of reinforcement learning in its episodic formulation. Episodes, however, are of limited value in many realistic domains; in others, they cannot be used at all. This is why we often prefer the much more flexible approach built around the idea of . and immediate rewards.作者: Exaggerate 時間: 2025-3-30 05:53
https://doi.org/10.1007/978-3-662-65083-7rom the same disease. Similar objects often belong to the same class—an observation underlying another popular approach to classification: when asked to determine the class of object ., find the training example most similar to it, and then label . with this similar example’s class.作者: 高度表 時間: 2025-3-30 11:14
https://doi.org/10.1007/978-3-642-94166-5e links that interconnect the neurons. The task for machine learning is to provide algorithms capable of finding weights that result in good classification behavior. This search is accomplished by a process commonly referred to as a neural network’s ..作者: 感情 時間: 2025-3-30 13:30
Rudolf Demel,Otto Hoche,Paul Moritschat it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking often disappoints the user. And so, even though this textbook does not want to be mathematical, it cannot help discussing at least the basic concepts of the ..作者: Lasting 時間: 2025-3-30 19:32 作者: 綁架 時間: 2025-3-31 00:34 作者: 廚房里面 時間: 2025-3-31 01:34 作者: OFF 時間: 2025-3-31 07:30
Au?enbeziehungen der Universit?t fail. For another, the excessive detail of available attributes may obscure vital information about the data. To cope with these complications, more advanced techniques are needed. This is why . was born.作者: stroke 時間: 2025-3-31 12:20 作者: Osteons 時間: 2025-3-31 13:33
Inter-Class Boundaries: Linear and Polynomial Classifiers,do a good job in simple domains where examples of the two classes are easy to separate. The more flexible high-order polynomials, capable of implementing complicated shapes of inter-class boundaries, have to be handled with care.作者: 符合國情 時間: 2025-3-31 20:59 作者: 夾死提手勢 時間: 2025-4-1 01:10 作者: gimmick 時間: 2025-4-1 05:44
https://doi.org/10.1007/978-3-642-94166-5ailment may have nothing to begin with save a few subjective symptoms. To narrow the field of possible diagnoses, she prescribes a few lab tests, and, based on their results, additional lab tests still. In other words, the doctor considers only “attributes” likely to add to her momentary understanding and ignores the remaining attributes.作者: Offensive 時間: 2025-4-1 06:21
https://doi.org/10.1007/978-3-7091-2188-7learning, as well. A group of classifiers is created, each of them somewhat different. When they vote about a class label, their “collective wisdom” often compensates for each individual’s imperfections. This results in higher classification performance.作者: cornucopia 時間: 2025-4-1 11:08
Anmerkungen und Zitatnachweise, is to be labeled in this or that concrete way. Typically, a classifier of this kind is represented not by a single rule, but by a set of rules, a .. Induction of rule-sets is capable of discovering recursive definitions, something that other machine-learning paradigms cannot do.作者: 沙漠 時間: 2025-4-1 17:51
