作者: 男學(xué)院 時(shí)間: 2025-3-21 20:29 作者: Promotion 時(shí)間: 2025-3-22 03:02 作者: 感情脆弱 時(shí)間: 2025-3-22 07:05
Regression final example, you can think of classification as a special case of regression, where we want to predict either +?1 or ??1; this isn’t usually the best way to proceed, however. Predicting values is very useful, and so there are many examples like this.作者: 前兆 時(shí)間: 2025-3-22 09:08
Learning to Classify free on the web, you would use a classifier to decide whether it was safe to run it (i.e., look at the program, and say yes or no according to some rule). As yet another example, credit card companies must decide whether a transaction is good or fraudulent.作者: 吹牛需要藝術(shù) 時(shí)間: 2025-3-22 15:28
A Little Learning Theoryis going to behave well on test—we need some reason to be confident that this is the case. It is possible to bound test error from training error. The bounds are all far too loose to have any practical significance, but their presence is reassuring.作者: 預(yù)兆好 時(shí)間: 2025-3-22 20:09
High Dimensional Datances, rather than correlations, because covariances can be represented in a matrix easily. High dimensional data has some nasty properties (it’s usual to lump these under the name “the curse of dimension”). The data isn’t where you think it is, and this can be a serious nuisance, making it difficult to fit complex probability models.作者: 解決 時(shí)間: 2025-3-22 23:31
Clustering Using Probability Models a natural way of obtaining soft clustering weights (which emerge from the probability model). And it provides a framework for our first encounter with an extremely powerful and general algorithm, which you should see as a very aggressive generalization of k-means.作者: miracle 時(shí)間: 2025-3-23 03:08
Regression: Choosing and Managing Modelsus chapter, we saw how to find outlying points and remove them. In Sect. 11.2, I will describe methods to compute a regression that is largely unaffected by outliers. The resulting methods are powerful, but fairly intricate.作者: 幼稚 時(shí)間: 2025-3-23 09:06
Textbook 2019 for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate?computer science programs in machine learning, this textbook is a?machine learni作者: AND 時(shí)間: 2025-3-23 13:09 作者: 欄桿 時(shí)間: 2025-3-23 17:56 作者: Mnemonics 時(shí)間: 2025-3-23 20:20
Learning Sequence Models Discriminativelyed to solve a problem, and modelling the letter conditioned on the ink is usually much easier (this is why classifiers work). Second, in many applications you would want to learn a model that produces the right sequence of hidden states given a set of observed states, as opposed to maximizing likelihood.作者: 廚師 時(shí)間: 2025-3-23 22:33 作者: Obloquy 時(shí)間: 2025-3-24 05:20
SpringerBriefs in Computer Scienceis going to behave well on test—we need some reason to be confident that this is the case. It is possible to bound test error from training error. The bounds are all far too loose to have any practical significance, but their presence is reassuring.作者: LAIR 時(shí)間: 2025-3-24 06:47
Studies in Fuzziness and Soft Computingnces, rather than correlations, because covariances can be represented in a matrix easily. High dimensional data has some nasty properties (it’s usual to lump these under the name “the curse of dimension”). The data isn’t where you think it is, and this can be a serious nuisance, making it difficult to fit complex probability models.作者: 繞著哥哥問 時(shí)間: 2025-3-24 12:05
S.-C. Fang,J. R. Rajasekera,H.-S. J. Tsao a natural way of obtaining soft clustering weights (which emerge from the probability model). And it provides a framework for our first encounter with an extremely powerful and general algorithm, which you should see as a very aggressive generalization of k-means.作者: 陶瓷 時(shí)間: 2025-3-24 16:39
Enthalpy and equations of state,us chapter, we saw how to find outlying points and remove them. In Sect. 11.2, I will describe methods to compute a regression that is largely unaffected by outliers. The resulting methods are powerful, but fairly intricate.作者: EVICT 時(shí)間: 2025-3-24 19:48 作者: Exterior 時(shí)間: 2025-3-25 03:10
Hidden Markov Modelsons (I got “meats,” “meat,” “fish,” “chicken,” in that order). If you want to produce random sequences of words, the next word should depend on some of the words you have already produced. A model with this property that is very easy to handle is a Markov chain (defined below).作者: 去世 時(shí)間: 2025-3-25 04:37 作者: Gyrate 時(shí)間: 2025-3-25 09:33
Principal Component Analysist model. Furthermore, representing a dataset like this very often suppresses noise—if the original measurements in your vectors are noisy, the low dimensional representation may be closer to the true data than the measurements are.作者: Induction 時(shí)間: 2025-3-25 14:43
major applied areas in?learning, including coverage of:.? classification using standard machinery (naive bayes; nearest?neighbor; SVM).? clustering and vector quantization (largely as in PSCS).? PCA (largely a978-3-030-18116-1978-3-030-18114-7作者: Essential 時(shí)間: 2025-3-25 18:09
Systematic Integration by Parts,t model. Furthermore, representing a dataset like this very often suppresses noise—if the original measurements in your vectors are noisy, the low dimensional representation may be closer to the true data than the measurements are.作者: 扔掉掐死你 時(shí)間: 2025-3-25 22:39
Low Rank Approximationsate points. This data matrix must have low rank (because the model is low dimensional) . it must be close to the original data matrix (because the model is accurate). This suggests modelling data with a low rank matrix.作者: MAIZE 時(shí)間: 2025-3-26 03:13
Clusteringblob parameters are and (b) which data points belong to which blob. Generally, we will collect together data points that are close and form blobs out of them. The blobs are usually called ., and the process is known as ..作者: 泄露 時(shí)間: 2025-3-26 06:23 作者: 被詛咒的人 時(shí)間: 2025-3-26 11:08 作者: KEGEL 時(shí)間: 2025-3-26 15:20
http://image.papertrans.cn/a/image/159911.jpg作者: aesthetic 時(shí)間: 2025-3-26 17:49 作者: 無動于衷 時(shí)間: 2025-3-26 21:08 作者: grandiose 時(shí)間: 2025-3-27 03:57
Gibbs paradox and degenerate gases, produce a second regression that fixes those errors. You may have dismissed this idea, though, because if one uses only linear regressions trained using least squares, it’s hard to see how to build a second regression that fixes the first regression’s errors.作者: miniature 時(shí)間: 2025-3-27 08:14 作者: 有害 時(shí)間: 2025-3-27 13:03
SVMs and Random ForestsAssume we have a labelled dataset consisting of . pairs (.., ..). Here .. is the .’th feature vector, and .. is the .’th class label. We will assume that there are two classes, and that .. is either 1 or ??1. We wish to predict the sign of . for any point ..作者: Ergots 時(shí)間: 2025-3-27 16:00
Cícero Nogueira dos Santos,Ruy Luiz Milidiúcause many problems are naturally classification problems. For example, if you wish to determine whether to place an advert on a webpage or not, you would use a classifier (i.e., look at the page, and say yes or no according to some rule). As another example, if you have a program that you found for作者: grenade 時(shí)間: 2025-3-27 21:31
SpringerBriefs in Computer Sciencedata predicts test error, and how training error predicts test error. Error on held-out training data is a very good predictor of test error. It’s worth knowing why this should be true, and Sect. 3.1 deals with that. Our training procedures assume that a classifier that achieves good training error 作者: Credence 時(shí)間: 2025-3-28 00:01
Studies in Fuzziness and Soft Computing is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean). The covariance matrix may be less familiar. This is a collection of all covariances between pairs of components. We use covaria作者: etiquette 時(shí)間: 2025-3-28 02:27 作者: FIS 時(shí)間: 2025-3-28 06:58 作者: 大暴雨 時(shí)間: 2025-3-28 11:13 作者: uveitis 時(shí)間: 2025-3-28 17:33
S.-C. Fang,J. R. Rajasekera,H.-S. J. Tsaoblob parameters are and (b) which data points belong to which blob. Generally, we will collect together data points that are close and form blobs out of them. The blobs are usually called ., and the process is known as ..作者: Between 時(shí)間: 2025-3-28 20:06 作者: Urea508 時(shí)間: 2025-3-29 00:56 作者: NIL 時(shí)間: 2025-3-29 03:10 作者: 折磨 時(shí)間: 2025-3-29 10:32
Gibbs paradox and degenerate gases, produce a second regression that fixes those errors. You may have dismissed this idea, though, because if one uses only linear regressions trained using least squares, it’s hard to see how to build a second regression that fixes the first regression’s errors.作者: Stricture 時(shí)間: 2025-3-29 13:19
Gibbs paradox and degenerate gases,is missing. I will use the sequence “I had a glass of red wine with my grilled xxxx.” What is the best guess for the missing word? You could obtain one possible answer by counting word frequencies, then replacing the missing word with the most common word. This is “the,” which is not a particularly 作者: nullify 時(shí)間: 2025-3-29 18:53 作者: 刻苦讀書 時(shí)間: 2025-3-29 22:56 作者: 瑣事 時(shí)間: 2025-3-30 03:22 作者: nocturia 時(shí)間: 2025-3-30 06:56 作者: 數(shù)量 時(shí)間: 2025-3-30 10:32 作者: Ambiguous 時(shí)間: 2025-3-30 15:10
High Dimensional Data is hard to plot, though Sect. 4.1 suggests some tricks that are helpful. Most readers will already know the mean as a summary (it’s an easy generalization of the 1D mean). The covariance matrix may be less familiar. This is a collection of all covariances between pairs of components. We use covaria作者: 拍翅 時(shí)間: 2025-3-30 16:48
Principal Component Analysistem, we can set some components to zero, and get a representation of the data that is still accurate. The rotation and translation can be undone, yielding a dataset that is in the same coordinates as the original, but lower dimensional. The new dataset is a good approximation to the old dataset. All作者: 爵士樂 時(shí)間: 2025-3-30 22:31
Low Rank Approximationsate points. This data matrix must have low rank (because the model is low dimensional) . it must be close to the original data matrix (because the model is accurate). This suggests modelling data with a low rank matrix.作者: 易于出錯 時(shí)間: 2025-3-31 01:40 作者: PAEAN 時(shí)間: 2025-3-31 06:32
