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» A kernel path algorithm for support vector machines
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DAGM
2010
Springer
13 years 10 months ago
Computational TMA Analysis and Cell Nucleus Classification of Renal Cell Carcinoma
Abstract. We consider an automated processing pipeline for tissue micro array analysis (TMA) of renal cell carcinoma. It consists of several consecutive tasks, which can be mapped ...
Peter J. Schüffler, Thomas J. Fuchs, Cheng So...
MINENET
2006
ACM
14 years 3 months ago
SVM learning of IP address structure for latency prediction
We examine the ability to exploit the hierarchical structure of Internet addresses in order to endow network agents with predictive capabilities. Specifically, we consider Suppor...
Robert Beverly, Karen R. Sollins, Arthur Berger
ICASSP
2011
IEEE
13 years 22 days ago
Building HMM based unit-selection speech synthesis system using synthetic speech naturalness evaluation score
This paper proposes a unit-selection and waveform concatenation speech synthesis system based on synthetic speech naturalness evaluation. A Support Vector Machine (SVM) and Log Li...
Heng Lu 0002, Zhen-Hua Ling, Li-Rong Dai, Ren-Hua ...
CVPR
2008
IEEE
14 years 11 months ago
Max Margin AND/OR Graph learning for parsing the human body
We present a novel structure learning method, Max Margin AND/OR Graph (MM-AOG), for parsing the human body into parts and recovering their poses. Our method represents the human b...
Long Zhu, Yuanhao Chen, Yifei Lu, Chenxi Lin, Alan...
NIPS
2003
13 years 10 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller