標(biāo)題: Titlebook: Ensembles in Machine Learning Applications; Oleg Okun,Giorgio Valentini,Matteo Re Book 2011 Springer Berlin Heidelberg 2011 Computational [打印本頁] 作者: 復(fù)雜 時(shí)間: 2025-3-21 18:05
書目名稱Ensembles in Machine Learning Applications影響因子(影響力)
書目名稱Ensembles in Machine Learning Applications影響因子(影響力)學(xué)科排名
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書目名稱Ensembles in Machine Learning Applications網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Ensembles in Machine Learning Applications被引頻次
書目名稱Ensembles in Machine Learning Applications被引頻次學(xué)科排名
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書目名稱Ensembles in Machine Learning Applications年度引用學(xué)科排名
書目名稱Ensembles in Machine Learning Applications讀者反饋
書目名稱Ensembles in Machine Learning Applications讀者反饋學(xué)科排名
作者: Original 時(shí)間: 2025-3-21 23:23
Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification,les. Therefore we propose voting with MBDS ensembles (VMBDSs).We show that the generalization performance of the VMBDSs ensembles improves with the number of MBDS classifiers. However this number can become large and thus the VMBDSs ensembles can have a computational-complexity problem as well. Fort作者: Corral 時(shí)間: 2025-3-22 03:25 作者: 從容 時(shí)間: 2025-3-22 04:42
An Improved Mixture of Experts Model: Divide and Conquer Using Random Prototypes,testing strategies of the standard HME model are also modified, based on the same insight applied to standard ME. In both cases, the proposed approach does not require to train the gating networks, as they are implemented with simple distance-based rules. In so doing the overall time required for tr作者: legislate 時(shí)間: 2025-3-22 12:04 作者: Palter 時(shí)間: 2025-3-22 14:25
https://doi.org/10.1007/978-3-030-76107-3les. Therefore we propose voting with MBDS ensembles (VMBDSs).We show that the generalization performance of the VMBDSs ensembles improves with the number of MBDS classifiers. However this number can become large and thus the VMBDSs ensembles can have a computational-complexity problem as well. Fort作者: Palter 時(shí)間: 2025-3-22 17:35
https://doi.org/10.1007/978-3-319-41585-7rformances are obtained with the semi-supervised data-driven network. However, combining it with the expertise-driven network improves performance in many cases and leads to interesting insights about the datasets, networks and metrics.作者: 修剪過的樹籬 時(shí)間: 2025-3-23 00:13 作者: 后天習(xí)得 時(shí)間: 2025-3-23 02:33 作者: 無瑕疵 時(shí)間: 2025-3-23 06:36
https://doi.org/10.1007/978-3-319-57306-9ection to maximize the amount of validation data considering, in turn, each fold as a validation fold to select the trees from. The aim is to increase performance by reducing the variance of the tree ensemble selection process. We demonstrate the effectiveness of our approach on several UCI and real-world data sets.作者: 天空 時(shí)間: 2025-3-23 12:05 作者: 細(xì)節(jié) 時(shí)間: 2025-3-23 16:36
Bias-Variance Analysis of ECOC and Bagging Using Neural Nets,o understand the overall trends when the parameters of the base classifiers – nodes and epochs for NNs –, are changed. We show experimentally on 5 artificial and 4 UCI MLR datasets that there are some clear trends in the analysis that should be taken into consideration while designing NN classifier systems.作者: 演講 時(shí)間: 2025-3-23 18:21
Fast-Ensembles of Minimum Redundancy Feature Selection,me prevents them from scaling up to real-world applications.We propose two methods which enhance correlation-based feature selection such that the stability of feature selection comes with little or even no extra runtime.We show the efficiency of the algorithms analytically and empirically on a wide range of datasets.作者: 暫停,間歇 時(shí)間: 2025-3-24 01:50
Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection,nce for feature selection criteria. We compare our performance in the causal structure learning problem to a collection of common feature selection methods.We also compare to Bayesian local structure learning. These results can also be easily extended to other casual structure models such as undirected graphical models.作者: Bravura 時(shí)間: 2025-3-24 02:46 作者: ADAGE 時(shí)間: 2025-3-24 09:27 作者: 音的強(qiáng)弱 時(shí)間: 2025-3-24 11:54 作者: Obsessed 時(shí)間: 2025-3-24 17:50 作者: aerial 時(shí)間: 2025-3-24 21:53
https://doi.org/10.1007/978-981-19-5106-0several ensemble methods: Bagging , Random Subspaces, AdaBoost.R2 and Iterated Bagging. For all the considered methods and variants, ensembles with Random Oracles are better than the corresponding version without the Oracles.作者: 調(diào)整 時(shí)間: 2025-3-25 02:15 作者: MOTTO 時(shí)間: 2025-3-25 06:40
Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers,characteristic curve (AUC) and false negative rate (FNR) of proposed algorithms are compared with correlation-based feature selection (FCBF and CFS) and causal based feature selection algorithms (PC, TPDA, GS, IAMB).作者: aggravate 時(shí)間: 2025-3-25 08:22
A Novel Ensemble Technique for Protein Subcellular Location Prediction,rature to solve this problem all the existing approaches are affected by some limitations, so that the problem is still open. Experimental results clearly indicate that the proposed technique, called ., performs equally, if not better, than state of the art ensemble methods aimed at multi-class classification of highly unbalanced data.作者: 易怒 時(shí)間: 2025-3-25 13:53 作者: Dedication 時(shí)間: 2025-3-25 18:23
Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests,ection to maximize the amount of validation data considering, in turn, each fold as a validation fold to select the trees from. The aim is to increase performance by reducing the variance of the tree ensemble selection process. We demonstrate the effectiveness of our approach on several UCI and real-world data sets.作者: Hiatal-Hernia 時(shí)間: 2025-3-25 23:13
Embedding Random Projections in Regularized Gradient Boosting Machines,ure Random Projections, normalized and uniform binary. Furthermore, we study the effect to keep or change the dimensionality of the data space. Experimental results performed on synthetic and UCI datasets show that Boosting methods with embedded random data projections are competitive to AdaBoost and Regularized Boosting.作者: FLUSH 時(shí)間: 2025-3-26 01:40
The Science of Construction Materialso understand the overall trends when the parameters of the base classifiers – nodes and epochs for NNs –, are changed. We show experimentally on 5 artificial and 4 UCI MLR datasets that there are some clear trends in the analysis that should be taken into consideration while designing NN classifier systems.作者: 大喘氣 時(shí)間: 2025-3-26 08:02
https://doi.org/10.1007/978-3-642-56257-0me prevents them from scaling up to real-world applications.We propose two methods which enhance correlation-based feature selection such that the stability of feature selection comes with little or even no extra runtime.We show the efficiency of the algorithms analytically and empirically on a wide range of datasets.作者: FAZE 時(shí)間: 2025-3-26 12:11 作者: Osteoarthritis 時(shí)間: 2025-3-26 15:31
https://doi.org/10.5822/978-1-61091-205-1strategy divides the training set based on a selected feature and trains a separate classifier for each subset. Experiments are carried out on simulated and real datasets. We report improvement in the final classification accuracy as a result of combining the three strategies.作者: Bureaucracy 時(shí)間: 2025-3-26 19:10
Marco Bertoldi,Paolo Sequi,Tiziano Papi public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers.作者: 摻假 時(shí)間: 2025-3-26 21:03
On the Design of Low Redundancy Error-Correcting Output Codes, public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers.作者: SEEK 時(shí)間: 2025-3-27 02:07 作者: 不能和解 時(shí)間: 2025-3-27 07:33 作者: 逢迎春日 時(shí)間: 2025-3-27 11:34
On the Design of Low Redundancy Error-Correcting Output Codes,essed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of 作者: 極少 時(shí)間: 2025-3-27 15:47
Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification,lass classification problem as a set of binary classification problems. Due to code redundancy ECOC schemes can significantly improve generalization performance on multi-class classification problems. However, they can face a computational complexity problem when the number of classes is large..In t作者: abracadabra 時(shí)間: 2025-3-27 20:16
Bias-Variance Analysis of ECOC and Bagging Using Neural Nets,gating (Bagging) and Error Correcting Output Coding (ECOC) ensembles using a biasvariance framework; and make comparisons with single classifiers, while having Neural Networks (NNs) as base classifiers. As the performance of the ensembles depends on the individual base classifiers, it is important t作者: observatory 時(shí)間: 2025-3-28 01:14 作者: Carminative 時(shí)間: 2025-3-28 02:56
Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers,lgorithms cannot scale up to deal with high dimensional data, that is more than few hundred features. This chapter presents hybrid correlation and causal feature selection for ensemble classifiers to deal with this problem. Redundant features are removed by correlation-based feature selection and th作者: 藥物 時(shí)間: 2025-3-28 07:20
Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection,ensemble masking measures can provide an approximate Markov Blanket. Consequently, an ensemble feature selection method can be used to learnMarkov Blankets for either discrete or continuous networks (without linear, Gaussian assumptions). We use masking measures for redundancy and statistical infere作者: 廣告 時(shí)間: 2025-3-28 11:55
Ensembles of Bayesian Network Classifiers Using Glaucoma Data and Expertise,tion of glaucoma, a major cause of blindness worldwide. We use visual field and retinal data to predict the early onset of glaucoma. In particular, the ability of BNs to deal with missing data allows us to select an optimal data-driven network by comparing supervised and semi-supervised models. An e作者: chastise 時(shí)間: 2025-3-28 16:33
A Novel Ensemble Technique for Protein Subcellular Location Prediction,rect Acyclic Graph (.). Each base classifier, called ., is mainly based on the projection of the given points on the Fisher subspace, estimated on the training data, by means of a novel technique. The proposed multiclass classifier is applied to the task of protein subcellular location prediction, w作者: 失望未來 時(shí)間: 2025-3-28 20:56 作者: abolish 時(shí)間: 2025-3-28 23:45 作者: 枯燥 時(shí)間: 2025-3-29 05:42 作者: AFFIX 時(shí)間: 2025-3-29 09:48
An Improved Mixture of Experts Model: Divide and Conquer Using Random Prototypes,y partitions the input space of a problem into a number of subspaces, experts becoming specialized on each subspace. To manage this process, theME uses an expert called gating network, which is trained together with the other experts. In this chapter, we propose a modified version of the ME algorith作者: 驚呼 時(shí)間: 2025-3-29 13:54
Three Data Partitioning Strategies for Building Local Classifiers,udy we experimentally investigate three strategies for building local classifiers that are based on different routines of sampling data for training. The first two strategies are based on clustering the training data and building an individual classifier for each cluster or a combination. The third 作者: 有角 時(shí)間: 2025-3-29 19:04
https://doi.org/10.1007/978-3-642-22910-7Computational Intelligence; Computational Intelligence; Ensembles in Machine Learning Applications; Ens作者: 殺人 時(shí)間: 2025-3-29 21:42
978-3-662-50706-3Springer Berlin Heidelberg 2011作者: NEEDY 時(shí)間: 2025-3-30 02:32 作者: 乞丐 時(shí)間: 2025-3-30 07:29
https://doi.org/10.1007/978-94-007-6683-9s (AUs). The method adopted is to train a single Error-Correcting Output Code (ECOC) multiclass classifier to estimate the probabilities that each one of several commonly occurring AU groups is present in the probe image. Platt scaling is used to calibrate the ECOC outputs to probabilities and appro作者: 抵消 時(shí)間: 2025-3-30 09:36 作者: malign 時(shí)間: 2025-3-30 14:43
