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检索条件"主题词=Baum-welch algorithm"

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Modelling Emotion Dynamics on Twitter via Hidden Markov Model 21

Modelling Emotion Dynamics on Twitter via Hidden Markov Mode...

引用

21st International Conference on Information Integration and Web-Based Applications and Services (iiWAS)

作者： Naskar, Debashis Onaindia, Eva Rebollo, Miguel Das, Subhashis Univ Politecn Valencia Valencia Spain Univ Trento Trento Italy

ISBN: (纸本)9781450371797

Exploring the mechanism about users' emotion dynamics towards social events and further predicting their future emotions have attracted great attention to the researchers. One of the unexplored components of human communication found online in written form is an emotional expression. However, despite the concreteness of the online expressions in written form, it remains unpredictable which kinds of emotions will be expressed in individual messages of Twitter users. To investigate this, we perform an investigation on observing emotions unfolding in a consecutive sequence of tweets for a particular user based on his/her past history. In this paper, we propose a method on given a set of tweets related with some events (identified by the usage of a hashtag), determines how those sentiments will be distributed on behalf of a person within a conversation. We present the Hidden Markov Model (HMM) to understand the nature of emotion dynamics in Twitter messages.

关键词： TESC HMM E-HMM baum-welch algorithm Emotion dynamics

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TOWARD ROBUST LEARNING OF THE GAUSSIAN MIXTURE STATE EMISSION DENSITIES FOR HIDDEN MARKOV MODELS

TOWARD ROBUST LEARNING OF THE GAUSSIAN MIXTURE STATE EMISSIO...

引用

2010 IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Tang, Hao Hasegawa-Johnson, Mark Huang, Thomas S. Univ Illinois Dept Elect & Comp Engn Urbana IL 61801 USA

ISBN: (纸本)9781424442966

One important class of state emission densities of the hidden Markov model (HMM) is the Gaussian mixture densities. The classical baum-welch algorithm often fails to reliably learn the Gaussian mixture densities when there is insufficient training data, due to the large number of free parameters present in the model. In this paper, we propose a novel strategy for robustly and accurately learning the Gaussian mixture state emission densities of the HMM. The strategy is based on an ensemble framework for probability density estimation in which the learning of the Gaussian mixture densities is formulated as a gradient descent search in a function space. The resulting learning algorithm is called "the boosting baum-welch algorithm." Our preliminary experiment results on emotion recognition from speech show that the proposed algorithm outperforms the original baum-welch algorithm on this task.

关键词： hidden Markov model Gaussian mixture density baum-welch algorithm boosting

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The Learning algorithms of Coupled Discrete Hidden Markov Models

The Learning Algorithms of Coupled Discrete Hidden Markov Mo...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Du, Shi Ping Wang, Jian Wei, Yu Ming Sichuan Agr Univ Coll Life & Basic Sci Yaan 625014 Sichuan Peoples R China Sichuan Agr Univ Triticeae Res Inst Yaan 625014 Sichuan Peoples R China

ISBN: (纸本)9783037858646

A hidden Markov model (HMM) encompasses a large class of stochastic process models and has been successfully applied to a number of scientific and engineering problems, including speech and other pattern recognition problems, and biological sequence analysis. A major restriction is found, however, in conventional HMM, i.e., it is ill-suited to capture the interactions among different models. A variety of coupled hidden Markov models (CHMMs) have recently been proposed as extensions of HMM to better characterize multiple interdependent sequences. The resulting models have multiple state variables that are temporally coupled via matrices of conditional probabilities. This paper study is focused on the coupled discrete HMM, there are two state variables in the network. By generalizing forward-backward algorithm, Viterbi algorithm and baum-welch algorithm commonly used in conventional HMM to accommodate two state variables, several new formulae solving the 2-chain coupled discrete HMM probability evaluation, decoding and training problem are theoretically derived.

关键词： Hidden Markov model Coupled hidden Markov model forward-backward procedure Viterbi algorithm baum-welch algorithm Lagrange multipliers

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Training Hidden Markov Models on Incomplete Sequences 13

Training Hidden Markov Models on Incomplete Sequences

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13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE)

作者： Popov, Alexander A. Gultyaeva, Tatyana A. Uvarov, Vadim E. Novosibirsk State Tech Univ Dept Theoret & Appl Informat Novosibirsk Russia

ISBN: (纸本)9781509040698

This paper deals with the algorithms of training hidden Markov models on sequences with missing observations. The method of imputation using Viterbi algorithm and the method of marginalization of missing observations are studied. These two methods are compared to the standard methods of dealing with missing data: imputation of gaps using the mode (the most frequent value) of nearest observations and gluing of observable parts of the sequence. The studied methods appeared to be more effective than the standard ones. Although the marginalization method proved to be a bit more effective than the method of imputation using Viterbi algorithm.

关键词： Hidden Markov models machine learning sequences baum-welch algorithm missing observations incomplete data

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Ocean Surface Stochastic Channel Modeling based on Hidden Markov Model 7

Ocean Surface Stochastic Channel Modeling based on Hidden Ma...

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7th IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Jiang, Kaijie Pan, Dawei Jiang, Tao Yuan, Yuqi Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Harbin Engn Univ Coll Automat Harbin Peoples R China

ISBN: (纸本)9781538656488

In this paper, we propose to apply Hidden Markov Model to modeling of microwave channel in sea surface. The microwave channel on the sea varies with the sea state, while the hidden -Markov model has enough universality. We give the general idea of using the HMM model to model the sea surface channel: Fitting the power and delay distribution of multipath propagation ray through HMM to establish the channel model.

关键词： Hidden Markov Model channel modeling baum-welch algorithm ray tracing

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A Block Diagonal Markov Model for Indoor Software-Defined Power Line Communication

A Block Diagonal Markov Model for Indoor Software-Defined Po...

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IEEE PES/IAS PowerAfrica Conference

作者： Familua, Ayokunle Damilola Univ Johannesburg Ctr Telecommun Dept Elect & Elect Engn Sci Johannesburg South Africa

ISBN: (纸本)9781728110103

A Semi-Hidden Markov Model (SHMM) for bursty error channels is defined by a state transition probability matrix A, a prior probability vector Pi, and the state dependent output symbol error probability matrix B. Several processes are utilized for estimating Lambda, Pi and B from a given empirically obtained or simulated error sequence. However, despite placing some restrictions on the underlying Markov model structure, we still have a computationally intensive estimation procedure, especially given a large error sequence containing long burst of identical symbols. Thus, in this paper, we utilize under some moderate assumptions, a Markov model with random state transition matrix A equivalent to a unique Block Diagonal Markov model with state transition matrix Lambda to model an indoor software-defined power line communication system. A computationally efficient modified baum-welch algorithm for estimation of Lambda given an experimentally obtained error sequence from the indoor PLC channel is utilized. Resulting Equivalent Block Diagonal Markov models assist designers to accelerate and facilitate the procedure of novel PLC systems design and evaluation.

关键词： baum-welch algorithm Block Diagonal Markov Model PLC Smart Grid Software defined NB-PLC

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COMPLEXITY REDUCTION USING POWER-LAW BASED SCHEDULING FOR EXPLOITING SPATIAL CORRELATION IN DISTRIBUTED VIDEO CODING

COMPLEXITY REDUCTION USING POWER-LAW BASED SCHEDULING FOR EX...

引用

15th IEEE International Conference on Image Processing (ICIP 2008)

作者： Misra, Kiran Karande, Shirish Desai, Keyur Radha, Hayder Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9781424417650

In pixel-domain distributed video coding (DVC), due to the largely translational nature of motion, residue errors in the side-information frame are often clustered together. These clusterings can be exploited to reduce the number of syndrome bits required to successfully perform Low Density Parity Check (LDPC) decoding, and therefore improve the overall rate-distortion performance. We shall see that using alternate iterations of LDPC syndrome decoding and baum-welch channel estimation proves to be an efficient scheme for exploiting the spatial clustering of errors in pixel-domain DVC. In this paper we demonstrate that a sparser power-law based scheduling of the channel estimation iteration leads to significant reduction in estimation complexity (around 83% reduction) for a small loss in rate-distortion performance (less than 0.75 dB). This sparser scheduling of channel estimation iterations can potentially improve decoding delays.

关键词： Distributed Video Coding Low-Density Parity-Check codes baum-welch algorithm

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N-best vector quantization for isolated word speech recognition

N-best vector quantization for isolated word speech recognit...

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Annual Conference on the Society-of-Instrument-and-Control-Engineers

作者： Nose, Masaya Maki, Shuichi Yartiane, Noburnoto Morikawa, Yoshitaka Okayama Univ Grad Sch Nat Sci & Technol Okayama Japan

ISBN: (纸本)9784907764289

Speech recognition is performed by utilizing acoustic and linguistic model. The contribution of this paper is improvement of acoustic model. Acoustic model is constructed by hidden Markov model (HMM). HMM has two representations, that are discrete HMM and continuous HMM. The former uses vector quantization (VQ), whereas the latter uses functions such as (mixture) Gaussian distribution. In Viterbi algorithm, VQ has advantage that it only operates by addition. However VQ also has a problem of distortion. This paper attempts to improve recognition precision in discrete HMM with modified VQ that gives multiple outputs for an input.

关键词： isolated word speech recognition VQ vector quantization discrete HMM acoustic model improvement baum-welch algorithm speaker-independent speech recognition

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Reinforcement Learning aided Smart-home Decision-making in an Interactive Smart Grid

Reinforcement Learning aided Smart-home Decision-making in a...

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IEEE Green Energy and Systems Conference (IGESC)

作者： Li, Ding Jayaweera, Sudharman K. Univ New Mexico Dept Elect & Comp Engn Albuquerque NM 87131 USA

ISBN: (纸本)9781479973330

In this paper, a complete hierarchical architecture is presented for the Utility-customer interaction, which tightly connect several important research topics, such as customer load prediction, renewable generation integration, power-load balancing and demand response. The complete interaction cycle consists of two stages: (1) Initial interaction (long-term planning) and (2) Real-time interaction (short-term planning). A hidden mode Markov decision process (HM-MDP) model is developed for customer real-time decision making, which outperforms the conventional Markov decision process (MDP) model in handling the non-stationary environment. To obtain a low-complexity, real-time algorithm, that allows to adaptively incorporate new observations as the environment changes, we resort to Q-learning based approximate dynamic programming (ADP). Without requiring specific starting and ending points of the scheduling period, the Q-learning algorithm offers more flexibility in practice. Performance analysis of both exact and approximate algorithms are presented with simulation results, in comparison with other decision making strategies.

关键词： Smart-home hidden mode Markov decision process (HM-MDP) baum-welch algorithm approximate dynamic programming (ADP) Q-learning

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A Comparison of Some Methods for Training Hidden Markov Models on Sequences with Missing Observations 11

A Comparison of Some Methods for Training Hidden Markov Mode...

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11th International Forum on Strategic Technology (IFOST)

作者： Popov, Alexander Gultyaeva, Tatyana Uvarov, Vadim Novosibirsk State Tech Univ Dept Appl Math & Comp Sci Novosibirsk Russia

ISBN: (纸本)9781509008551

The three approaches to the problem of hidden Markov models training on sequences with missing observations are discussed: marginalization of missing observations, gluing of available parts of the sequence and training on the multisequence formed from the available parts of the sequence. The training performance of the three approaches is evaluated for various numbers of gaps in training sequences. The results were compared to the standard imputation method based on the mode (the most frequent value) of nearest observations. The marginalization approach showed the best training accuracy. The multisequence approach demonstrated a very poor performance hence it is considered inapplicable. Both the marginalization method and the gluing method performed better than the mode imputation method. The dependence of training accuracy on the position of gaps in training sequence was investigated. It has been found that the biggest decrease in training accuracy is achieved when the gap is situated at the beginning or in the middle of the sequence while the lowest decrease is observed when it is situated at the end.

关键词： hidden Markov models machine learning sequence recognition baum-welch algorithm missing observations incomplete data

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