Journal ArticleDOI
On the memory complexity of the forward-backward algorithm
Reads0
Chats0
TLDR
A novel variation of the FB algorithm - called the Efficient Forward Filtering Backward Smoothing (EFFBS) - is proposed to reduce the memory complexity without the computational overhead.About:
This article is published in Pattern Recognition Letters.The article was published on 2010-01-01. It has received 51 citations till now. The article focuses on the topics: Worst-case complexity & Average-case complexity.read more
Citations
More filters
Journal ArticleDOI
A survey of techniques for incremental learning of HMM parameters
TL;DR: This paper underscores the need for empirical benchmarking studies among techniques presented in literature, and proposes several evaluation criteria based on non-parametric statistical testing to facilitate the selection of techniques given a particular application domain.
Journal ArticleDOI
Dynamic selection of generative-discriminative ensembles for off-line signature verification
TL;DR: In this article, a hybrid generative-discriminative ensembles of classifiers (EoCs) are proposed to design an off-line signature verification system from few samples, where the classifier selection process is performed dynamically.
Journal ArticleDOI
V2X Routing in a VANET Based on the Hidden Markov Model
TL;DR: P predictive routing based on the hidden Markov model (PRHMM) for VANETS, which exploits the regularity of vehicle moving behaviors to increase the transmission performance and enables seamless handoff between vehicle-to-vehicle and vehicle- to-infrastructure communications.
Journal ArticleDOI
An autonomous computation offloading strategy in Mobile Edge Computing: A deep learning-based hybrid approach
TL;DR: Simulation results show that the proposed hybrid model can appropriately fit the problem with near-optimal accuracy regarding the offloading decision-making, the latency, and the energy consumption predictions in the proposed self-management framework.
Journal ArticleDOI
Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates
TL;DR: A comprehensive overview of latent Markov (LM) models for the analysis of longitudinal categorical data is provided and methods for selecting the number of states and for path prediction are outlined.
References
More filters
Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains
Journal ArticleDOI
Statistical Inference for Probabilistic Functions of Finite State Markov Chains
Leonard E. Baum,Ted Petrie +1 more
Journal ArticleDOI
Matrix multiplication via arithmetic progressions
Don Coppersmith,Shmuel Winograd +1 more
TL;DR: In this article, a new method for accelerating matrix multiplication asymptotically is presented, based on the ideas of Volker Strassen, by using a basic trilinear form which is not a matrix product.