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» Sparseness Achievement in Hidden Markov Models
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NIPS
2008
13 years 9 months ago
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...
CCS
2007
ACM
14 years 1 months ago
Keystroke statistical learning model for web authentication
Keystroke typing characteristics is considered as one of the important biometric features that can be used to protect users against malicious attacks. In this paper we propose a s...
Cheng-Huang Jiang, Shiuhpyng Shieh, Jen-Chien Liu
CORR
2011
Springer
191views Education» more  CORR 2011»
13 years 2 months ago
A Message-Passing Receiver for BICM-OFDM over Unknown Clustered-Sparse Channels
We propose a factor-graph-based approach to joint channel-estimationand-decoding of bit-interleaved coded orthogonal frequency division multiplexing (BICM-OFDM). In contrast to ex...
Philip Schniter
ICA
2007
Springer
13 years 11 months ago
Infinite Sparse Factor Analysis and Infinite Independent Components Analysis
Abstract. A nonparametric Bayesian extension of Independent Components Analysis (ICA) is proposed where observed data Y is modelled as a linear superposition, G, of a potentially i...
David Knowles, Zoubin Ghahramani
ICML
2005
IEEE
14 years 8 months ago
Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields
We present a directed Markov random field (MRF) model that combines n-gram models, probabilistic context free grammars (PCFGs) and probabilistic latent semantic analysis (PLSA) fo...
Shaojun Wang, Shaomin Wang, Russell Greiner, Dale ...