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Voting experts: An unsupervised algorithm for segmenting sequences

Paul Cohen, Niall Adams, Brent Heeringa
2007 Intelligent Data Analysis  
We describe a statistical signature of chunks and an algorithm for finding chunks.  ...  The Voting-Experts exploits the signature of chunks to find word boundaries in text from four languages and episode boundaries in the activities of a mobile robot.  ...  Sara Nishi for collecting the corpus of Anime lyrics, and Aram Galstyan and Wesley Kerr for helpful discussions.  ... 
doi:10.3233/ida-2007-11603 fatcat:3zrzvcuanfe4xgcutzb7sxr26m

Hierarchical voting experts: An unsupervised algorithm for hierarchical sequence segmentation

Matthew Miller, Alexander Stoytchev
2008 2008 7th IEEE International Conference on Development and Learning  
Acknowledgements We would like to acknowledge Paul Cohen for giving us the source code for the original Voting Experts algorithm.  ...  Introduction This paper extends the Voting Experts (VE) algorithm (Cohen, Adams, & Heeringa 2007) to segment hierarchically structured sequences.  ...  Notice the improvement due to the third voting expert. Table 1 : 1 Performance Results for the HVE algorithm.  ... 
doi:10.1109/devlrn.2008.4640827 fatcat:5yzoucimdngtxnfrxadldjzjji

A robust unsupervised consensus control chart pattern recognition framework

Siavash Haghtalab, Petros Xanthopoulos, Kaveh Madani
2015 Expert systems with applications  
Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not robust enough for practical purposes.  ...  The majority of CCPR literature consists of supervised classification algorithms. Less studies consider unsupervised versions of the problem.  ...  Corpet used hierarchical clustering for an algorithm for the multiple alignment of sequences, either proteins or nucleic acids (Corpet, 1988 ).  ... 
doi:10.1016/j.eswa.2015.04.069 fatcat:6z3mgqvpabdsbk63vtfejm6y3i

Learning by aggregating experts and filtering novices: a solution to crowdsourcing problems in bioinformatics

Ping Zhang, Weidan Cao, Zoran Obradovic
2013 BMC Bioinformatics  
We proposed a probabilistic classification algorithm based on labels obtained by multiple noisy annotators.  ...  In many biomedical applications, there is a need for developing classification models based on noisy annotations.  ...  Majority Voting (MV), a commonly used approach for this problem, has a limitation that the aggregated label for an example is estimated locally by only estimating the labels assigned to that example and  ... 
doi:10.1186/1471-2105-14-s12-s5 pmid:24268030 fatcat:zeccjatdtzfkxpxg3442brhr6u

Prototyping a Traffic Light Recognition Device with Expert Knowledge

Thiago Almeida, Hendrik Macedo, Leonardo Matos, Nathanael Vasconcelos
2018 Information  
We argue that expert knowledge should be used to decrease the burden of collecting a huge amount of data for ML tasks.  ...  Results show an improvement in the accuracy rate around 15%.  ...  The traffic light is further classified by the next SVM, observing whether it has an arrow and its direction, using an '1-vs.-1' voting method.  ... 
doi:10.3390/info9110278 fatcat:ltvb2foqtrexrbvlh5ozzhty7q

Hierarchical Probabilistic Segmentation of Discrete Events

Guy Shani, Christopher Meek, Asela Gunawardana
2009 2009 Ninth IEEE International Conference on Data Mining  
In this paper we present an unsupervised learning algorithm for segmenting sequences of symbols or categorical events.  ...  Algorithms for segmenting mostly belong to the supervised learning family, where a labeled corpus is available to the algorithm in the learning phase.  ...  In this paper we present an unsupervised learning algorithm for segmenting sequences of symbols or categorical events.  ... 
doi:10.1109/icdm.2009.87 dblp:conf/icdm/ShaniMG09 fatcat:mxdhuemvd5effnraav3o4nap7e

Cardiac imaging: working towards fully-automated machine analysis & interpretation

Piotr J Slomka, Damini Dey, Arkadiusz Sitek, Manish Motwani, Daniel S Berman, Guido Germano
2017 Expert Review of Medical Devices  
It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice.  ...  Expert commentary-Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality.  ...  then taking a weighted majority vote of the sequence of classifiers.  ... 
doi:10.1080/17434440.2017.1300057 pmid:28277804 pmcid:PMC5450918 fatcat:u2sta7rplbcd5nj54ao2nhgliu

An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes [chapter]

Paul Cohen, Brent Heeringa, Niall M. Adams
2002 Lecture Notes in Computer Science  
This paper describes an unsupervised algorithm for segmenting categorical time series into episodes.  ...  The Voting-Experts algorithm first collects statistics about the frequency and boundary entropy of ngrams, then passes a window over the series and has two "expert methods" decide where in the window boundaries  ...  [8] give an unsupervised segmentation procedure for Japanese, however it too supposes known sequence boundaries.  ... 
doi:10.1007/3-540-45728-3_5 fatcat:ryxpjyas2bbehi5fdsnjh5vinu

Consensus Sequence Segmentation [article]

Tamal Chowdhury, Rabindra Rakshit, Arko Banerjee
2013 arXiv   pre-print
We compare our algorithm to previous approaches from unsupervised sequence segmentation literature and provide superior segmentation over number of benchmarks.  ...  Our linear time unsupervised algorithm relies entirely on statistical relationships among alphabets in the input sequence to detect location of word boundaries.  ...  W O R D 1 W O R W O R D 1 W O R W O R D 1 W O R D 2 Cohen et al proposed Bootstrap Voting Experts (BVE) [3] , an extension to the VE algorithm for unsupervised sequence segmentation.  ... 
arXiv:1308.3839v2 fatcat:jx52cdxvjjaxrpxea3b3c45jwe

A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

Xin Yu Liew, Nazia Hameed, Jeremie Clos
2021 Cancers  
A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer.  ...  Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.  ...  to a one-dimensional sequence for classification using Bi-LSTM [28] .  ... 
doi:10.3390/cancers13112764 fatcat:vew6qmw2w5dgne3ocdfwggfflu

Advancing Ensemble Learning Performance through Data Transformation and Classifiers Fusion in Granular Computing Context

Han Liu, Li Zhang
2019 Expert systems with applications  
classification for the instance through majority voting.  ...  In the abstract level, the rule of combination is referred to as vote, which simply counts the votes for each class and outputs the class that obtains the most votes.  ... 
doi:10.1016/j.eswa.2019.04.051 fatcat:uieuzp35lvdtfcs74vxiazjrru

Unsupervised hierarchical probabilistic segmentation of discrete events1

Guy Shani, Asela Gunawardana, Christopher Meek
2011 Intelligent Data Analysis  
In this paper we present an unsupervised learning algorithm for segmenting sequences of symbols or categorical events.  ...  We focus on unsupervised segmentation, where the algorithm never sees examples of successful segmentation, but still needs to discover meaningful segments.  ...  Empirical Evaluations In this section we provide an empirical comparison of three unsupervised algorithms for segmentation -our hierarchical multigram approach, Sequitur [16] , and Voting Experts [6]  ... 
doi:10.3233/ida-2011-0479 fatcat:lw3thcgqerfevbi5j7tdglwn34

Identifying Experts in Community Question Answering Website Based on Graph Convolutional Neural Network

Chen Liu, Yuchen Hao, Wei Shan, Zhihong Dai
2020 IEEE Access  
GCN-DOC MODEL Doc2vec, also known as paragraph vector, is an unsupervised algorithm proposed by Tomas mikolov [37] .  ...  These PageRank based methods, just like unsupervised algorithms, cannot be set specific identification goals.  ... 
doi:10.1109/access.2020.3012553 fatcat:vjxbd2ocx5ev7kg7shctmqxyee

A Deep Learning Approach for Expert Identification in Question Answering Communities [article]

Chen Zheng, Shuangfei Zhai, Zhongfei Zhang
2017 arXiv   pre-print
In this paper, we describe an effective convolutional neural network framework for identifying the expert in question answering community.  ...  The Top-1 accuracy results of our experiments show that our framework outperforms the best baseline framework for expert identification.  ...  Glove is an unsupervised learning algorithm for obtaining vector representations for words.  ... 
arXiv:1711.05350v1 fatcat:biuj5slnkrf3je2fvnvybt5sly

Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm

Matthew Miller, Peter Wong, Alexander Stoytchev
2009 Proceedings of the 2nd Conference on Artificial General Intelligence (2009)   unpublished
Voting Experts Voting Experts (VE) is an algorithm for the unsupervised segmentation of discrete token sequences.  ...  SUMMARY AND DISCUSSION In this thesis I have described a technique for the unsupervised segmentation of acoustic speech using the Voting Experts algorithm.  ... 
doi:10.2991/agi.2009.25 fatcat:pyzgm2h7gjgjphblkc65z3miba
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