標(biāo)題: Titlebook: An Introduction to Machine Learning; Miroslav Kubat Textbook 20151st edition Springer International Publishing Switzerland 2015 Applicatio [打印本頁] 作者: Awkward 時(shí)間: 2025-3-21 19:35
書目名稱An Introduction to Machine Learning影響因子(影響力)
書目名稱An Introduction to Machine Learning影響因子(影響力)學(xué)科排名
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
書目名稱An Introduction to Machine Learning年度引用
書目名稱An Introduction to Machine Learning年度引用學(xué)科排名
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: Galactogogue 時(shí)間: 2025-3-21 22:56 作者: aptitude 時(shí)間: 2025-3-22 00:38
https://doi.org/10.1007/978-3-662-26042-5uffer from the same disease. In short, similar objects often belong to the same class—an observation that forms the basis of a popular approach to classification: when asked to determine the class of object ., find the training example most similar to it. Then label . with this example’s class.作者: Chronic 時(shí)間: 2025-3-22 05:01 作者: labyrinth 時(shí)間: 2025-3-22 10:53
https://doi.org/10.1007/978-3-662-26042-5at it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking so often disappoints. And so, even though this textbook does not want to be mathematical, it cannot help introducing at least the basic concepts of the ..作者: Arable 時(shí)間: 2025-3-22 14:16 作者: 檢查 時(shí)間: 2025-3-22 17:46
https://doi.org/10.1007/978-3-663-02254-1 the training examples, but also future examples. Chapter?1 explained the principle of one of the most popular AI-based search techniques, the so-called ., and showed how it can be used in classifier induction.作者: 開始沒有 時(shí)間: 2025-3-22 23:09 作者: Insul島 時(shí)間: 2025-3-23 03:13 作者: 小木槌 時(shí)間: 2025-3-23 06:39
Computational Learning Theory,at it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking so often disappoints. And so, even though this textbook does not want to be mathematical, it cannot help introducing at least the basic concepts of the ..作者: 粗糙濫制 時(shí)間: 2025-3-23 12:52 作者: tolerance 時(shí)間: 2025-3-23 16:14
The Genetic Algorithm, the training examples, but also future examples. Chapter?1 explained the principle of one of the most popular AI-based search techniques, the so-called ., and showed how it can be used in classifier induction.作者: 鞭打 時(shí)間: 2025-3-23 21:47
https://doi.org/10.1007/978-3-319-20010-1Applications; bayesian classifiers; boosting; computational learning theory; decision trees; genetic algo作者: tendinitis 時(shí)間: 2025-3-23 22:27
978-3-319-34886-5Springer International Publishing Switzerland 2015作者: Sleep-Paralysis 時(shí)間: 2025-3-24 04:21 作者: 裙帶關(guān)系 時(shí)間: 2025-3-24 07:39 作者: 軌道 時(shí)間: 2025-3-24 12:59 作者: Jogging 時(shí)間: 2025-3-24 15:06 作者: 開始沒有 時(shí)間: 2025-3-24 20:34
Probabilities: Bayesian Classifiers,The earliest attempts to predict an example’s class based on the known attribute values go back to well before World War II—prehistory, by the standards of computer science. Of course, nobody used the term “machine learning,” in those days, but the goal was essentially the same as the one addressed in this book.作者: mortgage 時(shí)間: 2025-3-25 00:48 作者: 規(guī)范要多 時(shí)間: 2025-3-25 05:51 作者: Lipoprotein 時(shí)間: 2025-3-25 10:53
https://doi.org/10.1007/978-3-662-26042-5regions different from those occupied by negative examples. This observation motivates yet another approach to classification. Instead of the probabilities and similarities employed by the earlier paradigms, we can try to identify the . that separates the two classes. A very simple possibility is to作者: 陳腐的人 時(shí)間: 2025-3-25 14:40
https://doi.org/10.1007/978-3-662-26042-5erfit noisy training data, and because of the sometimes impractically high number of trainable parameters. Much more popular are . where many simple units, called ., are interconnected by weighted links into larger structures of remarkably high performance.作者: commensurate 時(shí)間: 2025-3-25 16:09
https://doi.org/10.1007/978-3-662-26042-5ws. Thus a physician seeking to come to grips with the nature of her patient’s condition often has nothing to begin with save a few subjective symptoms. And so, to narrow the field of diagnoses, she prescribes lab tests, and, based on the results, perhaps other tests still. At any given moment, then作者: Suggestions 時(shí)間: 2025-3-25 22:00
https://doi.org/10.1007/978-3-662-26042-5at it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking so often disappoints. And so, even though this textbook does not want to be mathematical, it cannot help introducing at least the basic concepts of the ..作者: probate 時(shí)間: 2025-3-26 02:34
https://doi.org/10.1007/978-3-662-26042-5behind a textbook’s toy domains has a way of complicating things, frustrating the engineer with unexpected obstacles, and challenging everybody’s notion of what exactly the induced classifier is supposed to do and why. Just as in any other field of technology, success is hard to achieve without a he作者: Nutrient 時(shí)間: 2025-3-26 08:13
https://doi.org/10.1007/978-3-662-26042-5ge, offering diverse points of view that complement each other to the point where they may inspire innovative solutions. Something similar can be done in machine learning, too. A group of classifiers is created in a way that makes each of them somewhat different. When they vote about the recommended作者: Irrigate 時(shí)間: 2025-3-26 11:24 作者: 憂傷 時(shí)間: 2025-3-26 14:49 作者: 性上癮 時(shí)間: 2025-3-26 17:07 作者: 歌劇等 時(shí)間: 2025-3-27 00:10 作者: CRANK 時(shí)間: 2025-3-27 02:23 作者: vibrant 時(shí)間: 2025-3-27 08:18
Inter-Class Boundaries: Linear and Polynomial Classifiers,regions different from those occupied by negative examples. This observation motivates yet another approach to classification. Instead of the probabilities and similarities employed by the earlier paradigms, we can try to identify the . that separates the two classes. A very simple possibility is to作者: BROW 時(shí)間: 2025-3-27 11:19 作者: Condescending 時(shí)間: 2025-3-27 15:42 作者: Kidney-Failure 時(shí)間: 2025-3-27 18:57
Computational Learning Theory,at it takes to induce a useful classifier from data, and, conversely, why the outcome of a machine-learning undertaking so often disappoints. And so, even though this textbook does not want to be mathematical, it cannot help introducing at least the basic concepts of the ..作者: progestogen 時(shí)間: 2025-3-27 22:12 作者: Tractable 時(shí)間: 2025-3-28 04:00
Induction of Voting Assemblies,ge, offering diverse points of view that complement each other to the point where they may inspire innovative solutions. Something similar can be done in machine learning, too. A group of classifiers is created in a way that makes each of them somewhat different. When they vote about the recommended作者: ROOF 時(shí)間: 2025-3-28 07:29 作者: Countermand 時(shí)間: 2025-3-28 11:56
Statistical Significance,omes up . eight times out of ten, any reasonable experimenter will suspect this to be nothing but a fluke, expecting that another set of ten tosses will give a result closer to reality. Similar caution is in place when measuring classification performance. To evaluate classification accuracy on a te作者: Pudendal-Nerve 時(shí)間: 2025-3-28 17:23
The Genetic Algorithm, the training examples, but also future examples. Chapter?1 explained the principle of one of the most popular AI-based search techniques, the so-called ., and showed how it can be used in classifier induction.作者: 輕快帶來危險(xiǎn) 時(shí)間: 2025-3-28 20:54
Reinforcement Learning,echniques that have been developed with this in mind. In ., though, the task is different. Instead of induction from a set of pre-classified examples, the agent “experiments” with a system, and the system responds to this experimentation with rewards or punishments. The agent then optimizes its beha作者: 嚴(yán)厲批評 時(shí)間: 2025-3-29 00:13
Textbook 20151st editionnear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms..作者: 愛哭 時(shí)間: 2025-3-29 06:04 作者: gorgeous 時(shí)間: 2025-3-29 09:56 作者: FIR 時(shí)間: 2025-3-29 15:23
https://doi.org/10.1007/978-3-662-26042-5ities and similarities employed by the earlier paradigms, we can try to identify the . that separates the two classes. A very simple possibility is to use to this end a linear function. More flexible are high-order polynomials which are capable of defining very complicated inter-class boundaries. These, however, have to be handled with care.作者: 倒轉(zhuǎn) 時(shí)間: 2025-3-29 17:50 作者: 灌溉 時(shí)間: 2025-3-29 23:45
https://doi.org/10.1007/978-3-662-26042-5 simple. Error rate rarely paints the whole picture, and there are situations in which it can even be misleading. This is why the conscientious engineer wants to be acquainted with other criteria to assess the classifiers’ performance. This knowledge will enable her to choose the one that is best in capturing the behavioral aspects of interest.作者: 動作謎 時(shí)間: 2025-3-30 02:31 作者: 喧鬧 時(shí)間: 2025-3-30 07:19
Die Umweltvertr?glichkeitsprüfung the agent “experiments” with a system, and the system responds to this experimentation with rewards or punishments. The agent then optimizes its behavior, its goal being to maximize the rewards and to minimize the punishments.作者: 自傳 時(shí)間: 2025-3-30 12:18
Inter-Class Boundaries: Linear and Polynomial Classifiers,ities and similarities employed by the earlier paradigms, we can try to identify the . that separates the two classes. A very simple possibility is to use to this end a linear function. More flexible are high-order polynomials which are capable of defining very complicated inter-class boundaries. These, however, have to be handled with care.作者: Meager 時(shí)間: 2025-3-30 16:13
