An HMM-based synthetic view generator to improve the efficiency of ensemble systems

2019 ◽  
Vol 28 (1) ◽  
pp. 4-18
Author(s):  
L Borrajo ◽  
A Seara Vieira ◽  
E L Iglesias
Keyword(s):  
Input Data ◽  
Markov Models ◽  
Multiple Views ◽  
Single Model ◽  
Single View ◽  
Ensembles Of Classifiers ◽  
Text Corpora ◽  
Standard Text ◽  
Synthetic View ◽  
Ensemble Systems

Abstract One of the most active areas of research in semi-supervised learning has been to study methods for constructing good ensembles of classifiers. Ensemble systems are techniques that create multiple models and then combine them to produce improved results. These systems usually produce more accurate solutions than a single model would. Specially, multi-view ensemble systems improve the accuracy of text classification because they optimize the functions to exploit different views of the same input data. However, despite being more promising than the single-view approaches, document datasets often have no natural multiple views available. This study proposes an algorithm to generate a synthetic view from a standard text dataset. The model generates a new view from the standard bag-of-words approach using an algorithm based on hidden Markov models (HMMs). To show the effectiveness of the proposed HMM-based synthetic view generation method, it has been integrated in a co-training ensemble system and tested with four text corpora: Reuters, 20 Newsgroup, TREC Genomics and OHSUMED. The results obtained are promising, showing a significant increase in the efficiency of the ensemble system compared to a single-view approach.

An HMM-Based Multi-view Co-training Framework for Single-View Text Corpora

2016 ◽  
pp. 66-78
Author(s):  
Eva Lorenzo Iglesias ◽  
Adrían Seara Vieira ◽  
Lourdes Borrajo Diz
Keyword(s):  
Single View ◽  
Text Corpora

Computational Intelligence for Modeling Human Sensations in Virtual Environments

2004 ◽  
Vol 8 (3) ◽  
pp. 302-312
Author(s):  
Ka Keung Lee ◽  
◽  
Yangsheng Xu
Keyword(s):  
Virtual Environments ◽  
Computational Intelligence ◽  
Input Data ◽  
Markov Models ◽  
Body Motion ◽  
Input Selection ◽  
Physical Stimuli ◽  
The Relationship ◽  
Relative Similarity ◽  
Full Body

In this research, computational intelligence techniques are applied towards the modeling of human sensations in virtual environments. We specifically focus on the following important questions: (1) how to efficiently model the relationship between human sensations and the physical stimuli presented to humans, (2) how to validate the human sensation models, and (3) how to reduce the size of the input data when it gets large and how to select the information which is most important to human sensation modeling. In order to provide an experimental testbed for the implementation of the proposed learning and analysis techniques, a full-body motion virtual reality interface capable of recording human sensations is developed. We propose using cascade neural networks with node-decoupled extended Kalman filter training for modeling human sensation in virtual environments. For the purpose of sensation model validation, we propose using a stochastic similarity measure based on hidden Markov models to calculate the relative similarity between model-generated sensation and actual human sensation. Next, we investigate a number of feature extraction and input selection techniques for reducing the input data size in human sensation modeling. We propose and develop a new input selection method based on independent component analysis, which is capable of reducing the data size and selecting the stimuli information that is most important to the human sensation.

scholarly journals Learning a Mid-Level Representation for Multiview Action Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Cuiwei Liu ◽  
Zhaokui Li ◽  
Xiangbin Shi ◽  
Chong Du
Keyword(s):  
Random Forest ◽  
Action Recognition ◽  
Random Forests ◽  
Random Forest Classifier ◽  
Multiple Views ◽  
Human Actions ◽  
Single View ◽  
Learning Framework ◽  
Camera View ◽  
Action Category

Recognizing human actions in videos is an active topic with broad commercial potentials. Most of the existing action recognition methods are supposed to have the same camera view during both training and testing. And thus performances of these single-view approaches may be severely influenced by the camera movement and variation of viewpoints. In this paper, we address the above problem by utilizing videos simultaneously recorded from multiple views. To this end, we propose a learning framework based on multitask random forest to exploit a discriminative mid-level representation for videos from multiple cameras. In the first step, subvolumes of continuous human-centered figures are extracted from original videos. In the next step, spatiotemporal cuboids sampled from these subvolumes are characterized by multiple low-level descriptors. Then a set of multitask random forests are built upon multiview cuboids sampled at adjacent positions and construct an integrated mid-level representation for multiview subvolumes of one action. Finally, a random forest classifier is employed to predict the action category in terms of the learned representation. Experiments conducted on the multiview IXMAS action dataset illustrate that the proposed method can effectively recognize human actions depicted in multiview videos.

COMPRESSION AND RECOGNITION OF SPATIO-TEMPORAL SEQUENCES FROM CONTEMPORARY BALLET

2006 ◽  
Vol 20 (05) ◽  
pp. 727-745 ◽  
Author(s):  
FREDERIC CHENEVIÈRE ◽  
SAMIA BOUKIR ◽  
BERTRAND VACHON
Keyword(s):  
Input Data ◽  
Markov Models ◽  
Recognition System ◽  
Polygonal Approximation ◽  
Recognition Method ◽  
Motion Trajectories ◽  
Efficient Representation ◽  
Spatio Temporal ◽  
Temporal Sequences ◽  
Direct Use

We aim at recognizing a set of dance gestures from contemporary ballet. Our input data are motion trajectories followed by the joints of a dancing body provided by a motion-capture system. It is obvious that direct use of the original signals is unreliable and expensive. Therefore, we propose a suitable tool for nonuniform sub-sampling of spatio-temporal signals. The key to our approach is the use of polygonal approximation to provide a compact and efficient representation of motion trajectories. Our dance gesture recognition method involves a set of Hidden Markov Models (HMMs), each of them being related to a motion trajectory followed by the joints. The recognition of such movements is then achieved by matching the resulting gesture models with the input data via HMMs. We have validated our recognition system on 12 fundamental movements from contemporary ballet performed by four dancers.

Part-of-Speech Tagging

2012 ◽  
Author(s):  
Atro Voutilainen
Keyword(s):  
Markov Models ◽  
Language Model ◽  
Language Models ◽  
Symbolic Language ◽  
Part Of Speech Tagging ◽  
Part Of Speech ◽  
Text Corpora ◽  
History Of ◽  
General Architecture ◽  
Speech Tagging

This article outlines the recently used methods for designing part-of-speech taggers; computer programs for assigning contextually appropriate grammatical descriptors to words in texts. It begins with the description of general architecture and task setting. It gives an overview of the history of tagging and describes the central approaches to tagging. These approaches are: taggers based on handwritten local rules, taggers based on n-grams automatically derived from text corpora, taggers based on hidden Markov models, taggers using automatically generated symbolic language models derived using methods from machine tagging, taggers based on handwritten global rules, and hybrid taggers, which combine the advantages of handwritten and automatically generated taggers. This article focuses on handwritten tagging rules. Well-tagged training corpora are a valuable resource for testing and improving language model. The text corpus reminds the grammarian about any oversight while designing a rule.

Market Segments and Stochastic Brand Choice Models

1976 ◽  
Vol 13 (1) ◽  
pp. 34-45 ◽  
Author(s):  
Robert C. Blattberg ◽  
Subrata K. Sen
Keyword(s):  
Panel Data ◽  
Markov Models ◽  
Brand Choice ◽  
Choice Models ◽  
Model Parameters ◽  
Buyer Behavior ◽  
Consumer Heterogeneity ◽  
Single Model ◽  
Market Segments

The modeling of buyer behavior by stochastic brand choice models has typically involved the use of a single model to represent the behavior of all consumers though consumer heterogeneity is recognized by allowing the model's parameters to vary across the population. However, analysis of panel data for several frequently purchased products indicates the existence of several distinct consumer segments which are difficult to represent by a single model. It is shown, instead, that in order to describe adequately the behavior of these segments, it is necessary to use several different models while allowing consumers within a segment to have different model parameters. It is further shown that simple heterogeneous multinomial and Markov models appear to be adequate to represent the behavior of most of the segments.

ROBUST SPEECH RECOGNITION USING A NOISE REJECTION APPROACH

1999 ◽  
Vol 08 (01) ◽  
pp. 53-71
Author(s):  
EMDAD KHAN ◽  
ROBERT LEVINSON
Keyword(s):  
Speech Recognition ◽  
Markov Models ◽  
Input Sequence ◽  
Recognition System ◽  
Multiple Views ◽  
Noisy Environment ◽  
Dynamic Features ◽  
Noise Rejection ◽  
Cycle Training ◽  
Noise Tolerant

In this paper, we explore some new approaches to improve speech recognition accuracy in a noisy environment. The key approaches taken are: (a) use no additional data (i.e. use only speakers data, no data for noise) for training and (b) no adaptation phase for noise. Instead of making adaptation in the recognition, preprocessing or both stages, we make a noise tolerant (rejection) speech recognition system where the system tries to reject noise automatically because of its inherent structure. We call our approach a noise rejection-based approach. Noise rejection is achieved by using multiple views and dynamic features of the input sequences. Multiple views exploit more information from the available data that is used for training multiple HMMs (Hidden Markov Models). This makes the training process simpler, faster and avoids the need to use a noise database, which is often difficult to obtain. The dynamic features (added to the HMM using vector emission probabilities) add more information about the input speech during training. Since the values of dynamic features of noise are usually much smaller than that of the speech signal, it helps reject the noise during recognition. Multiple views (we also call these scrambles) can be used at different stages in the recognition processes. This paper explore these possibilities. Also, multiple views of the input sequence are applied to multiple HMMs during recognition and the outcome of the multiple HMMs are combined using maximum evidence criterion. The accuracy of the noise rejection-based approach is further improved by using Higher Level Decision Making (HLD) - our method for data fusion. HLD improves accuracy by efficiently resolving conflicts. The key approaches taken for HLD are: meta reasoning, single cycle training (SCT), confidence factors and view minimization. Our tests show very encouraging results.

Moving Object Tracking Using Multiple Views and Data Association

2014 ◽  
Vol 666 ◽  
pp. 226-229
Author(s):  
Young Shin Ahn ◽  
Ahmed Mohammed ◽  
Jae Ho Choi
Keyword(s):  
Object Tracking ◽  
Performance Improvement ◽  
Data Association ◽  
Moving Object ◽  
The Other ◽  
Multiple Views ◽  
Single View ◽  
Extra Information ◽  
Significant Performance ◽  
Moving Object Tracking

In this paper, a moving object tracking method using multiple views of the same scene taken by three cameras are presented. The object of interest is the pedestrian in the street. Unlike the single view method, the proposed multi-view method can effectively exploit extra information available in the other views when the object of interest in one of the views falls into an occlusion or clutter environment. Making a relation of an object detected in one view with the ones in the other views is critical and it is obtained by data association. The simulation results show a significant performance improvement over the conventional one.

scholarly journals Multi-View Collaborative Network Embedding

2021 ◽  
Vol 15 (3) ◽  
pp. 1-18
Author(s):  
Sezin Kircali Ata ◽  
Yuan Fang ◽  
Min Wu ◽  
Jiaqi Shi ◽  
Chee Keong Kwoh ◽  
...  
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Second Order ◽  
Collaborative Network ◽  
Multiple Views ◽  
Network Embedding ◽  
Single View ◽  
Video Sharing ◽  
Low Dimensional

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose M ulti-view coll A borative N etwork E mbedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE , an attention -based extension of MANE, to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.

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