Journal ArticleDOI
Pattern Recognition and Machine Learning
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TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
Citations
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Journal ArticleDOI
Automatically Designing CNN Architectures Using the Genetic Algorithm for Image Classification
TL;DR: This article proposes an automatic CNN architecture design method by using genetic algorithms, to effectively address the image classification tasks and shows the very comparable classification accuracy to the best one from manually designed and automatic + manually tuning CNNs, while consuming fewer computational resources.
Proceedings ArticleDOI
Learning to share visual appearance for multiclass object detection
TL;DR: A hierarchical classification model that allows rare objects to borrow statistical strength from related objects that have many training examples and learns both a hierarchy for sharing visual appearance across 200 object categories and hierarchical parameters is presented.
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Predictive Markers for AD in a Multi-Modality Framework: An Analysis of MCI Progression in the ADNI Population
TL;DR: Whether the multi-modal disease marker (MMDM) can predict conversion from Mild Cognitive Impairment to AD is examined, and experiments reveal that this measure shows significant group differences between MCI subjects who progressed to AD, and those who remained stable for 3 years.
Journal ArticleDOI
What are artificial neural networks
TL;DR: Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction.
Proceedings ArticleDOI
Patch Group Based Nonlocal Self-Similarity Prior Learning for Image Denoising
TL;DR: It is demonstrated that, owe to the learned PG-GMM, a simple weighted sparse coding model, which has a closed-form solution, can be used to perform image denoising effectively, resulting in high PSNR measure, fast speed, and particularly the best visual quality among all competing methods.