標(biāo)題: Titlebook: Advanced Lectures on Machine Learning; ML Summer Schools 20 Olivier Bousquet,Ulrike Luxburg,Gunnar R?tsch Textbook 2004 Springer-Verlag Ber [打印本頁] 作者: credit 時(shí)間: 2025-3-21 19:18
書目名稱Advanced Lectures on Machine Learning影響因子(影響力)
書目名稱Advanced Lectures on Machine Learning影響因子(影響力)學(xué)科排名
書目名稱Advanced Lectures on Machine Learning網(wǎng)絡(luò)公開度
書目名稱Advanced Lectures on Machine Learning網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advanced Lectures on Machine Learning被引頻次
書目名稱Advanced Lectures on Machine Learning被引頻次學(xué)科排名
書目名稱Advanced Lectures on Machine Learning年度引用
書目名稱Advanced Lectures on Machine Learning年度引用學(xué)科排名
書目名稱Advanced Lectures on Machine Learning讀者反饋
書目名稱Advanced Lectures on Machine Learning讀者反饋學(xué)科排名
作者: 情感脆弱 時(shí)間: 2025-3-21 23:03
Gaussian Processes in Machine Learning,ign and manufacture. The bolted cantilever, which is the model for a bolted joint between a column and a base on a planer, was used to calrify the relationship between the interface pressure and the logarithmic damping decrement on the bolted joint in the different connecting conditions, such as var作者: 亞當(dāng)心理陰影 時(shí)間: 2025-3-22 03:10 作者: 付出 時(shí)間: 2025-3-22 04:41
Stochastic Learning,rnment present the latest research and breakthroughs on how biotechnology is being used to produce economically competitive fuels and chemicals in a sustainable and environmentally responsible manner. The contributors discuss both fundamental science discoveries and the progress that has been made i作者: OPINE 時(shí)間: 2025-3-22 12:19
https://doi.org/10.1007/b100712algorithmic learning; bayesian inference; classification; classifier systmes; inductive inference; learni作者: explicit 時(shí)間: 2025-3-22 16:40 作者: 高歌 時(shí)間: 2025-3-22 18:08
Advanced Lectures on Machine Learning978-3-540-28650-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: GEAR 時(shí)間: 2025-3-22 21:44
Big Data Application Architecture,er distinct labeling of the data (supervised learning), division of the data into classes (unsupervised learning), selection of the most significant features of the data (feature selection), or a combination of more than one of these tasks.作者: 山間窄路 時(shí)間: 2025-3-23 03:17 作者: 偽書 時(shí)間: 2025-3-23 06:01
Seyed Mehrshad Parvin Hosseini,Aydin Azizi define a distribution over functions. We present the simple equations for incorporating training data and examine how to learn the hyperparameters using the marginal likelihood. We explain the practical advantages of Gaussian Process and end with conclusions and a look at the current trends in GP work.作者: 徹底明白 時(shí)間: 2025-3-23 09:44 作者: 假設(shè) 時(shí)間: 2025-3-23 15:46 作者: verdict 時(shí)間: 2025-3-23 18:16 作者: sleep-spindles 時(shí)間: 2025-3-23 22:44
Big Data, Simulations and HPC ConvergenceThe goal of statistical learning theory is to study, in a statistical framework, the properties of learning algorithms. In particular, most results take the form of so-called error bounds. This tutorial introduces the techniques that are used to obtain such results.作者: Petechiae 時(shí)間: 2025-3-24 04:23
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/145819.jpg作者: 偶像 時(shí)間: 2025-3-24 09:40
Big Data Application Architecture,er distinct labeling of the data (supervised learning), division of the data into classes (unsupervised learning), selection of the most significant features of the data (feature selection), or a combination of more than one of these tasks.作者: SMART 時(shí)間: 2025-3-24 11:55 作者: averse 時(shí)間: 2025-3-24 16:10 作者: Fecundity 時(shí)間: 2025-3-24 22:02
Accelerating BigBench on Hadoopated from information theoretic and Bayesian principles. We briefly review basic models in unsupervised learning, including factor analysis, PCA, mixtures of Gaussians, ICA, hidden Markov models, state-space models, and many variants and extensions. We derive the EM algorithm and give an overview of作者: 鳥籠 時(shí)間: 2025-3-25 03:04 作者: landmark 時(shí)間: 2025-3-25 03:59
Big Data, Simulations and HPC Convergence been introduced making it possible to establish simple and powerful inequalities. These inequalities are at the heart of the mathematical analysis of various problems in machine learning and made it possible to derive new efficient algorithms. This text attempts to summarize some of the basic tools作者: Regurgitation 時(shí)間: 2025-3-25 09:01
Textbook 2004recognition; they provide in-depth overviews of exciting new developments and contain a large number of references...Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning..作者: 有幫助 時(shí)間: 2025-3-25 11:45 作者: Fierce 時(shí)間: 2025-3-25 18:58 作者: 多節(jié) 時(shí)間: 2025-3-25 21:47 作者: 法官 時(shí)間: 2025-3-26 01:08
Some Notes on Applied Mathematics for Machine Learning,and shows the variables and potential behaviour patterns that will arise as a consequence of the value of these variables. The consequences chart is being used a as a predictive tool and will be extended to audit the organisational characteristics of manufacturing companies.作者: 燦爛 時(shí)間: 2025-3-26 07:49 作者: 澄清 時(shí)間: 2025-3-26 11:09
Bayesian Inference: An Introduction to Principles and Practice in Machine Learning,978-3-322-94774-1作者: Oafishness 時(shí)間: 2025-3-26 14:34
0302-9743 urers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning..978-3-540-23122-6978-3-540-28650-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 者變 時(shí)間: 2025-3-26 19:06
Accelerating BigBench on Hadoopebraic calculations are typically replaced with simple calculations in the sample domain. One should however bear in mind that these are . of the true quantity of interest. An important scenario where Monte Carlo methods can be of great help is when one is interested in evaluating expectations of fu作者: Irrigate 時(shí)間: 2025-3-26 23:09
Unsupervised Learning,imple approximate analysis given. The mean equivalent yield stress, Y., of the composite was the total strength of the composite and calculated on the basis of relative volumetric proportion of each material in the composite. Some of the typical features of the process hitherto not reported are also presented and the results commented upon.作者: 擁護(hù) 時(shí)間: 2025-3-27 03:57
Stochastic Learning,ing pollution and waste disposal problems and their adverse impacts on global climate change. Cutting-edge and authoritative, Biotechnology for Fuels and Chemicals: The Twenty-Fifth Symposium provides an excellent overview of current research and development in the production of commodity fuels and 作者: intelligible 時(shí)間: 2025-3-27 08:37
6樓作者: burnish 時(shí)間: 2025-3-27 10:50
6樓作者: 細(xì)胞膜 時(shí)間: 2025-3-27 15:02
7樓作者: Diverticulitis 時(shí)間: 2025-3-27 18:21
7樓作者: 兇兆 時(shí)間: 2025-3-28 01:32
7樓作者: 刪減 時(shí)間: 2025-3-28 03:24
7樓作者: rectum 時(shí)間: 2025-3-28 06:24
8樓作者: 舊石器時(shí)代 時(shí)間: 2025-3-28 11:04
8樓作者: indenture 時(shí)間: 2025-3-28 16:56
8樓作者: 小故事 時(shí)間: 2025-3-28 19:32
8樓作者: BULLY 時(shí)間: 2025-3-29 01:11
9樓作者: 欄桿 時(shí)間: 2025-3-29 05:05
9樓作者: 領(lǐng)先 時(shí)間: 2025-3-29 08:25
9樓作者: synovium 時(shí)間: 2025-3-29 13:23
10樓作者: capsule 時(shí)間: 2025-3-29 19:09
10樓作者: FLAT 時(shí)間: 2025-3-29 23:04
10樓作者: Minuet 時(shí)間: 2025-3-30 02:16
10樓
