Journal ArticleDOI
Pattern Recognition and Machine Learning
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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Book
Model-Based Machine Learning
TL;DR: It is shown how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and a large-scale commercial application of this framework involving tens of millions of users is outlined.
Journal ArticleDOI
Large-Scale Cross-Modality Search via Collective Matrix Factorization Hashing
TL;DR: The experimental results demonstrate that CMFH can significantly outperform several state-of-the-art cross-modality Hashing methods, which validates the effectiveness of the proposed CMFH.
Journal ArticleDOI
Unsupervised Methods for Speaker Diarization: An Integrated and Iterative Approach
TL;DR: An improved clustering method is integrated with an existing re-segmentation algorithm and an iterative optimization scheme is implemented that demonstrates the ability to improve both speaker cluster assignments and segmentation boundaries in an unsupervised manner.
Proceedings ArticleDOI
Enforcing convexity for improved alignment with constrained local models
TL;DR: This paper proposes a new approach for optimizing the global warp update in an efficient manner by enforcing convexity at each local patch response surface and shows that the classic Lucas-Kanade approach to gradient descent image alignment can be viewed as a special case of this proposed framework.
Journal ArticleDOI
Impressionism, expressionism, surrealism: Automated recognition of painters and schools of art
TL;DR: A method for automated recognition of painters and schools of art based on their signature styles is described and its ability to automatically associate different artists that share the same school of art in an unsupervised fashion is described.