標(biāo)題: Titlebook: An Introduction to Machine Learning; Miroslav Kubat Textbook 20172nd edition Springer International Publishing AG 2017 Bayesian classifier [打印本頁] 作者: Inoculare 時(shí)間: 2025-3-21 16:12
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: Expostulate 時(shí)間: 2025-3-21 23:59 作者: 幻想 時(shí)間: 2025-3-22 00:42 作者: 填滿 時(shí)間: 2025-3-22 05:29
Die Unbeherrschtheit bei Platon 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 class, their “collective wisdom” often compensates for each individual’s imperfections.作者: Nefarious 時(shí)間: 2025-3-22 10:17 作者: 教育學(xué) 時(shí)間: 2025-3-22 13:35 作者: 清晰 時(shí)間: 2025-3-22 17:21 作者: 配置 時(shí)間: 2025-3-22 22:13 作者: savage 時(shí)間: 2025-3-23 01:53
Induction of Voting Assemblies, 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 class, their “collective wisdom” often compensates for each individual’s imperfections.作者: 針葉類的樹 時(shí)間: 2025-3-23 06:31
Performance Evaluation, 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: 強(qiáng)所 時(shí)間: 2025-3-23 13:27 作者: 思想上升 時(shí)間: 2025-3-23 15:33 作者: 嘮叨 時(shí)間: 2025-3-23 19:58 作者: dermatomyositis 時(shí)間: 2025-3-24 00:29
Textbook 20172nd editionmples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way作者: 易達(dá)到 時(shí)間: 2025-3-24 05:37 作者: 古文字學(xué) 時(shí)間: 2025-3-24 07:17 作者: Eulogy 時(shí)間: 2025-3-24 12:05 作者: Affluence 時(shí)間: 2025-3-24 18:49
https://doi.org/10.1007/978-3-8350-9083-5at 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: 并置 時(shí)間: 2025-3-24 21:40 作者: 輕打 時(shí)間: 2025-3-25 02:49
Die Unbeherrschtheit bei Platonge, 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: 可商量 時(shí)間: 2025-3-25 04:49 作者: diskitis 時(shí)間: 2025-3-25 09:27
https://doi.org/10.1007/978-3-476-05629-0omes 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: Conquest 時(shí)間: 2025-3-25 14:51 作者: HACK 時(shí)間: 2025-3-25 17:13
,Der Begriff Akrasia (?κρασ?α), the.-part. Typically, the classifier is represented not by a single rule, but by a set of rules, a.. The paradigm has certain advantages. For one thing, the rules capture the underlying logic, and therefore facilitate explanations of why an example has to be labeled with the given class; for anothe作者: hedonic 時(shí)間: 2025-3-25 23:22 作者: Banquet 時(shí)間: 2025-3-26 03:07
Similarities: Nearest-Neighbor Classifiers,uffer 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: terazosin 時(shí)間: 2025-3-26 05:24
Artificial Neural Networks,erfit 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: aesthetician 時(shí)間: 2025-3-26 08:48
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: 利用 時(shí)間: 2025-3-26 13:41
A Few Instructive Applications,behind 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 healthy dose of creativity.作者: 飛來飛去真休 時(shí)間: 2025-3-26 20:42 作者: 領(lǐng)巾 時(shí)間: 2025-3-26 22:41
Miroslav KubatOffers frequent opportunities to practice techniques with control questions, exercises, thought experiments, and computer assignments..Reinforces principles using well-selected toy domains and relevan作者: Ceremony 時(shí)間: 2025-3-27 04:29 作者: 惹人反感 時(shí)間: 2025-3-27 07:42
https://doi.org/10.1007/978-3-8350-9083-5uffer 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: 詢問 時(shí)間: 2025-3-27 12:12 作者: 善辯 時(shí)間: 2025-3-27 16:55 作者: 悲觀 時(shí)間: 2025-3-27 19:55
Die Unbeherrschtheit bei Platonbehind 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 healthy dose of creativity.作者: antiquated 時(shí)間: 2025-3-27 23:42
https://doi.org/10.1007/978-3-476-05629-0 the training examples, but also future examples. Chapter?. 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: BALE 時(shí)間: 2025-3-28 02:45
Die Unabh?ngigkeit des AbschlussprüfersYou will find it difficult to describe your mother’s face accurately enough for your friend to recognize her in a supermarket. But if you show him a few of her photos, he will immediately spot the tell-tale traits he needs. As they say, a picture—an example—is worth a thousand words.作者: Oversee 時(shí)間: 2025-3-28 09:49
Rechnungswesen und UnternehmensüberwachungThe 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: 造反,叛亂 時(shí)間: 2025-3-28 12:59
https://doi.org/10.1007/978-3-8350-9083-5When representing the training examples with points in an .-dimensional instance space, we may realize that positive examples tend to be clustered in regions different from those occupied by negative examples. This observation motivates yet another approach to classification.作者: 工作 時(shí)間: 2025-3-28 16:17 作者: 不足的東西 時(shí)間: 2025-3-28 22:18 作者: 大都市 時(shí)間: 2025-3-29 01:23
https://doi.org/10.1007/978-3-476-05629-0The fundamental problem addressed by this book is how to induce a classifier capable of determining the class of an object. We have seen quite a few techniques that have been developed with this in mind.作者: Ballerina 時(shí)間: 2025-3-29 04:03 作者: 沙文主義 時(shí)間: 2025-3-29 10:53
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: cancer 時(shí)間: 2025-3-29 13:04
Inter-Class Boundaries: Linear and Polynomial Classifiers,When representing the training examples with points in an .-dimensional instance space, we may realize that positive examples tend to be clustered in regions different from those occupied by negative examples. This observation motivates yet another approach to classification.作者: Acumen 時(shí)間: 2025-3-29 18:37 作者: jaundiced 時(shí)間: 2025-3-29 20:48 作者: LINES 時(shí)間: 2025-3-30 02:45 作者: 組裝 時(shí)間: 2025-3-30 06:22 作者: Arbitrary 時(shí)間: 2025-3-30 11:16 作者: 我吃花盤旋 時(shí)間: 2025-3-30 14:04 作者: Incommensurate 時(shí)間: 2025-3-30 17:47
Textbook 20172nd editionnduction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work..作者: 描繪 時(shí)間: 2025-3-31 00:08 作者: acetylcholine 時(shí)間: 2025-3-31 03:27 作者: Neuropeptides 時(shí)間: 2025-3-31 08:34
Decision Trees,ws. 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: BILK 時(shí)間: 2025-3-31 12:28
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: Costume 時(shí)間: 2025-3-31 16:59
A Few Instructive Applications,behind 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: 玷污 時(shí)間: 2025-3-31 20:56 作者: photopsia 時(shí)間: 2025-4-1 00:50
