دانلود رایگان مقاله لاتین اثر خود اصلاحی با مدل اجماع نهفته از سایت الزویر
عنوان فارسی مقاله:
اثرکلی خود اصلاحی با استفاده از مدل اجماع نهفته
عنوان انگلیسی مقاله:
Self-correcting ensemble using a latent consensus model
سال انتشار : 2016
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مقدمه انگلیسی مقاله:
1. Introduction
In the real world, people obtain professional advice from several experts before finally deciding on significant matters such as investing financially, seeking treatment for a disease, and buying products. Combining the opinions of several different specialists is natural. In artificial intelligence and data mining, ensemble systems are techniques that combine multiple experts opinions (e.g., classifier) to obtain better predictive performance than using a single opinion. Ensemble methods are also known as multiple classifier systems, committee of classifiers, or mixture of experts. Using the ensemble technique has several advantages. First, ensemble learning improves accuracy and robustness better than a single model does. Each classifier in an ensemble may capture the big picture of the problem and the ensemble technique may obtain more sensitive results by making up for each weak learner. Combining diverse, independent multiple predictors reduces variance and bias because of less dependence on the outliers of the training sets, which increases functional flexibility. This combination may also reduce the total error when each error occurs in different directions. The ensemble technique reduces the risk of selecting a poor classifier by averaging the outputs.Second, the ensemble technique is suitable for cases when the size of the data set to be analyzed is extremely large or extremely small. With the rapid development of hardware and software technologies, the size of data increases at a fast rate. Applying existing data mining techniques to large data is difficult [20]. An extremely small size of an available data set is problematic. Ensemble techniques can be useful when the size ofthe data setis extremely small because these techniques reproduce the training data set by using a resampling technique. Third, ensemble systems provide the means to solve difficult problems. The complex decision boundary that divides data sets cannot be learned by using a simple linear model. However, an ensemble can learn the complex boundary or function by appropriately combining simple classifiers.
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کلمات کلیدی:
Self-correcting ensemble using a latent consensus model Free ... isi-dl.com/item/101073 ... thejns.org, wiley.com, worldscientific.com. SEARCH. Self-correcting ensemble using a latent consensus model. sciencedirect.com. Source Link ... Abstract - SLCF slcf.snu.ac.kr/abstract.html Self-correcting ensemble using a latent consensus model ... To compare the performance of the proposed method with existing methods, experiments are ... Publications - Welcome to SLCF slcf.snu.ac.kr/ipublications.html Active learning using transductive sparse Bayesian regression. Author: Youngdoo Son and ... Self-correcting ensemble using a latent consensus model Publication insights: Voronoi Cell-Based Clustering Using a Kernel ... https://www.researchgate.net/.../273393209_Voronoi_Cell-Based_Clustering_Using_a_... Support-based clustering using kernels suffers from serious computational limitations inherent in ... Self-correcting Ensemble Using a Latent Consensus Model. Namhyoung Kim - Publications - ResearchGate https://www.researchgate.net/profile/Namhyoung_Kim/publications Article: Self-correcting Ensemble Using a Latent Consensus Model. Article · Jun 2016 ... Article · Sep 2014 · Expert Systems with Applications. Hyejin Park ... Adaptive Mixtures of Local Experts | Neural Computation | MIT Press ... www.mitpressjournals.org › List of Issues by RA Jacobs - 1991 - Cited by 3501 - Related articles Mar 13, 2008 - (2016) Self-correcting ensemble using a latent consensus model. Applied ... (2016) Robust mixture of experts modeling using the <mml:math ... Recurrent Coevolutionary Feature Embedding Processes for ... https://openreview.net/forum?id=HyWWpw5ex Nov 4, 2016 - Abstract: Recommender systems often use latent features to explain the .... process models, such as Hawkes and self-correcting processes.
