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114
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ICPR
2008
IEEE
15 years 9 months ago
Embedding HMM's-based models in a Euclidean space: The topological hidden Markov models
One of the major limitations of HMM-based models is the inability to cope with topology: When applied to a visible observation (VO) sequence, HMM-based techniques have difficulty ...
Djamel Bouchaffra
135
Voted
KDD
2004
ACM
166views Data Mining» more  KDD 2004»
16 years 3 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
179
Voted
AMDO
2006
Springer
15 years 7 months ago
Predicting 3D People from 2D Pictures
Abstract. We propose a hierarchical process for inferring the 3D pose of a person from monocular images. First we infer a learned view-based 2D body model from a single image using...
Leonid Sigal, Michael J. Black
KDD
2012
ACM
254views Data Mining» more  KDD 2012»
13 years 5 months ago
Playlist prediction via metric embedding
Digital storage of personal music collections and cloud-based music services (e.g. Pandora, Spotify) have fundamentally changed how music is consumed. In particular, automatically...
Shuo Chen, Josh L. Moore, Douglas Turnbull, Thorst...
GECCO
2003
Springer
139views Optimization» more  GECCO 2003»
15 years 8 months ago
Daily Stock Prediction Using Neuro-genetic Hybrids
We propose a neuro-genetic daily stock prediction model. Traditional indicators of stock prediction are utilized to produce useful input features of neural networks. The genetic al...
Yung-Keun Kwon, Byung Ro Moon