標題: Titlebook: Ensemble Machine Learning; Methods and Applicat Cha Zhang,Yunqian Ma Book 2012 Springer Science+Business Media, LLC 2012 Bagging Predictors [打印本頁] 作者: chondrocyte 時間: 2025-3-21 17:40
書目名稱Ensemble Machine Learning影響因子(影響力)
書目名稱Ensemble Machine Learning影響因子(影響力)學科排名
書目名稱Ensemble Machine Learning網絡公開度
書目名稱Ensemble Machine Learning網絡公開度學科排名
書目名稱Ensemble Machine Learning被引頻次
書目名稱Ensemble Machine Learning被引頻次學科排名
書目名稱Ensemble Machine Learning年度引用
書目名稱Ensemble Machine Learning年度引用學科排名
書目名稱Ensemble Machine Learning讀者反饋
書目名稱Ensemble Machine Learning讀者反饋學科排名
作者: 補角 時間: 2025-3-22 00:04
Book 2012is volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including the random forest skeleton tracking algorithm in the Xbox Kinect sensor, which bypasses the need for game controllers. At once a solid theoretical study and a practical guide, the volume is a windfall for researchers and practitioners alike..作者: HEED 時間: 2025-3-22 02:20
978-1-4899-8817-1Springer Science+Business Media, LLC 2012作者: corpuscle 時間: 2025-3-22 06:17
The Sales Sat Nav for Media Consultantsny of the simple classifiers alone. A . (WL) is a learning algorithm capable of producing classifiers with probability of error strictly (but only slightly) less than that of random guessing (0.5, in the binary case). On the other hand, a . (SL) is able (given enough training data) to yield classifiers with arbitrarily small error probability.作者: obligation 時間: 2025-3-22 12:41 作者: 繞著哥哥問 時間: 2025-3-22 15:35
https://doi.org/10.1007/b106381ithm which considers the cooperation and interaction among the ensemble members. NCL introduces a correlation penalty term into the cost function of each individual learner so that each learner minimizes its mean-square-error (MSE) error together with the correlation with other ensemble members.作者: 繞著哥哥問 時間: 2025-3-22 17:06 作者: PALL 時間: 2025-3-22 21:56
Targeted Learning,probability distributions .. One refers to . as the statistical model for .. We consider so called semiparametric models that cannot be parameterized by a finite dimensional Euclidean vector. In addition, suppose that our target parameter of interest is a parameter ., so that ψ. = .(.) denotes the parameter value of interest.作者: 撫慰 時間: 2025-3-23 01:45
Ensemble Learning by Negative Correlation Learning,ithm which considers the cooperation and interaction among the ensemble members. NCL introduces a correlation penalty term into the cost function of each individual learner so that each learner minimizes its mean-square-error (MSE) error together with the correlation with other ensemble members.作者: bonnet 時間: 2025-3-23 06:20 作者: 察覺 時間: 2025-3-23 13:18 作者: fatty-acids 時間: 2025-3-23 15:50
https://doi.org/10.1007/978-1-4419-9326-7Bagging Predictors; Basic Boosting; Ensemble learning; Object Detection; classification algorithm; deep n作者: nostrum 時間: 2025-3-23 18:45 作者: 粗糙 時間: 2025-3-24 00:26
The Sales Sat Nav for Media Consultantsny of the simple classifiers alone. A . (WL) is a learning algorithm capable of producing classifiers with probability of error strictly (but only slightly) less than that of random guessing (0.5, in the binary case). On the other hand, a . (SL) is able (given enough training data) to yield classifi作者: grenade 時間: 2025-3-24 02:54 作者: needle 時間: 2025-3-24 09:12
The Salience of Marketing Stimuliprobability distributions .. One refers to . as the statistical model for .. We consider so called semiparametric models that cannot be parameterized by a finite dimensional Euclidean vector. In addition, suppose that our target parameter of interest is a parameter ., so that ψ. = .(.) denotes the p作者: Detonate 時間: 2025-3-24 14:01
https://doi.org/10.1057/9780230338074 [6], Random Forests are an extension of Breiman’s bagging idea [5] and were developed as a competitor to boosting. Random Forests can be used for either a categorical response variable, referred to in [6] as “classification,” or a continuous response, referred to as “regression.” Similarly, the pre作者: intangibility 時間: 2025-3-24 17:55
https://doi.org/10.1007/b106381ithm which considers the cooperation and interaction among the ensemble members. NCL introduces a correlation penalty term into the cost function of each individual learner so that each learner minimizes its mean-square-error (MSE) error together with the correlation with other ensemble members.作者: Baffle 時間: 2025-3-24 19:09
https://doi.org/10.1007/978-1-4419-5987-4f kernel matrices. The Nystr?m method is a popular technique to generate low-rank matrix approximations but it requires sampling of a large number of columns from the original matrix to achieve good accuracy. This chapter describes a new family of algorithms based on mixtures of Nystr?m approximatio作者: GRIN 時間: 2025-3-25 03:00 作者: 生意行為 時間: 2025-3-25 05:07 作者: manifestation 時間: 2025-3-25 09:23
https://doi.org/10.1007/978-3-0348-0712-8scriminative learning methods for detection and segmentation of anatomical structures. In particular, we propose innovative detector structures, namely Probabilistic Boosting Network (PBN) and Marginal Space Learning (MSL), to address the challenges in anatomical structure detection. We also present作者: 控訴 時間: 2025-3-25 15:44
https://doi.org/10.1007/1-4020-5742-3oinformatics, the Random Forest (RF) [6] technique, which includes an ensemble of decision trees and incorporates feature selection and interactions naturally in the learning process, is a popular choice. It is nonparametric, interpretable, efficient, and has high prediction accuracy for many types 作者: 愛了嗎 時間: 2025-3-25 16:22
Book 2012. Dubbed “ensemble learning” by researchers in computational intelligence and machine learning, it is known to improve a decision system’s robustness and accuracy. Now, fresh developments are allowing researchers to unleash the power of ensemble learning in an increasing range of real-world applicat作者: Blood-Clot 時間: 2025-3-25 23:02 作者: foodstuff 時間: 2025-3-26 03:47
Discriminative Learning for Anatomical Structure Detection and Segmentation, a regression approach called Shape Regression Machine (SRM) for anatomical structure detection. For anatomical structure segmentation, we propose discriminative formulations, explicit and implicit, that are based on classification, regression and ranking.作者: ALOFT 時間: 2025-3-26 08:10 作者: Jubilation 時間: 2025-3-26 11:32
https://doi.org/10.1007/978-1-4419-5987-4bounds guaranteeing a better convergence rate than the standard Nystr?m method is also presented. Finally, experiments with several datasets containing up to 1 M points are presented, demonstrating significant improvement over the standard Nystr?m approximation.作者: 不透氣 時間: 2025-3-26 12:37
,Ensemble Nystr?m,bounds guaranteeing a better convergence rate than the standard Nystr?m method is also presented. Finally, experiments with several datasets containing up to 1 M points are presented, demonstrating significant improvement over the standard Nystr?m approximation.作者: 終止 時間: 2025-3-26 17:47
ros and cons of various ensemble learning methods.Demonstrat.It is common wisdom that gathering a variety of views and inputs improves the process of decision making, and, indeed, underpins a democratic society. Dubbed “ensemble learning” by researchers in computational intelligence and machine lear作者: scrape 時間: 2025-3-27 00:26
The Salesforce Consultant’s Guidehe output is obtained by aggregating through majority voting. Boosting is a . ensemble scheme, in the sense the weight of an observation at step . depends (only) on the step . ? 1. It appears clear that we obtain a specific boosting scheme when we choose a loss function, which orientates the data re-weighting mechanism, and a weak learner.作者: 樂器演奏者 時間: 2025-3-27 01:09
https://doi.org/10.1057/9780230338074her a categorical response variable, referred to in [6] as “classification,” or a continuous response, referred to as “regression.” Similarly, the predictor variables can be either categorical or continuous.作者: 放大 時間: 2025-3-27 07:23
https://doi.org/10.1057/9780230598324rious illumination and background conditions), researchers generally learn a classifier that can distinguish an image patch that contains the object of interest from all other image patches. Ensemble learning methods have been very successful in learning classifiers for object detection.作者: 材料等 時間: 2025-3-27 10:17
Boosting Kernel Estimators,he output is obtained by aggregating through majority voting. Boosting is a . ensemble scheme, in the sense the weight of an observation at step . depends (only) on the step . ? 1. It appears clear that we obtain a specific boosting scheme when we choose a loss function, which orientates the data re-weighting mechanism, and a weak learner.作者: Expertise 時間: 2025-3-27 14:43
Random Forests,her a categorical response variable, referred to in [6] as “classification,” or a continuous response, referred to as “regression.” Similarly, the predictor variables can be either categorical or continuous.作者: Immortal 時間: 2025-3-27 20:53
Object Detection,rious illumination and background conditions), researchers generally learn a classifier that can distinguish an image patch that contains the object of interest from all other image patches. Ensemble learning methods have been very successful in learning classifiers for object detection.作者: 托人看管 時間: 2025-3-28 01:31
https://doi.org/10.1007/978-1-4471-2068-1ying and evaluating crucial parts of the surgical procedures, and providing the medical specialists with useful feedback [2]. Similarly, these systems can help us improve our productivity in office environments by detecting various interesting and important events around us to enhance our involvement in important office tasks [21].作者: Landlocked 時間: 2025-3-28 04:57 作者: GRILL 時間: 2025-3-28 09:30 作者: 生氣地 時間: 2025-3-28 10:25 作者: 含糊 時間: 2025-3-28 15:31
Ensemble Learning,ms, such as feature selection, confidence estimation, missing feature, incremental learning, error correction, class-imbalanced data, learning concept drift from nonstationary distributions, among others. This chapter provides an overview of ensemble systems, their properties, and how they can be applied to such a wide spectrum of applications.作者: Gnrh670 時間: 2025-3-28 19:35
Ensemble Learning,elligence and machine learning community. This attention has been well deserved, as ensemble systems have proven themselves to be very effective and extremely versatile in a broad spectrum of problem domains and real-world applications. Originally developed to reduce the variance—thereby improving t作者: trigger 時間: 2025-3-29 02:03
Boosting Algorithms: A Review of Methods, Theory, and Applications,ny of the simple classifiers alone. A . (WL) is a learning algorithm capable of producing classifiers with probability of error strictly (but only slightly) less than that of random guessing (0.5, in the binary case). On the other hand, a . (SL) is able (given enough training data) to yield classifi作者: 承認 時間: 2025-3-29 06:29 作者: dandruff 時間: 2025-3-29 11:15
Targeted Learning,probability distributions .. One refers to . as the statistical model for .. We consider so called semiparametric models that cannot be parameterized by a finite dimensional Euclidean vector. In addition, suppose that our target parameter of interest is a parameter ., so that ψ. = .(.) denotes the p作者: CHART 時間: 2025-3-29 13:08 作者: 完全 時間: 2025-3-29 19:09
