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Regularized Semi-Supervised Latent Dirichlet Allocation for visual concept learning

Liansheng Zhuang, Haoyuan Gao, Jiebo Luo, Zhouchen Lin
2013 Neurocomputing  
In this paper, to take advantage of both limited labeled training images and rich unlabeled images, we propose a novel regularized Semi-Supervised Latent Dirichlet Allocation (r-SSLDA) for learning visual  ...  Instead of introducing a new complex topic model, we attempt to find an efficient way to learn topic models in a semi-supervised way.  ...  This work is partially supported by the National  ... 
doi:10.1016/j.neucom.2012.04.043 fatcat:skkisypofbfohaa5jplws657bq

Semi-Supervised Linear Discriminant Clustering

Chien-Liang Liu, Wen-Hoar Hsaio, Chia-Hoang Lee, Fu-Sheng Gou
2014 IEEE Transactions on Cybernetics  
To exploit the information brought by unlabeled examples, this paper proposes to use soft labels to denote the labels of unlabeled examples.  ...  This paper devises a semi-supervised learning method called semi-supervised linear discriminant clustering (Semi-LDC).  ...  Semi-supervised classification employs labeled data along with unlabeled data to construct a more accurate classifier; while semi-supervised clustering uses a few labeled data to bias the clustering of  ... 
doi:10.1109/tcyb.2013.2278466 pmid:23996591 fatcat:dpxxp6lcyraxhb2rzrbryy2pqa

SoK: Applying Machine Learning in Security - A Survey [article]

Heju Jiang, Jasvir Nagra, Parvez Ahammad
2016 arXiv   pre-print
The idea of applying machine learning(ML) to solve problems in security domains is almost 3 decades old.  ...  Based on our survey, we also suggest a point of view that treats security as a game theory problem instead of a batch-trained ML problem.  ...  We point to research challenges that will improve, enhance, and expand our understanding, designs, and efficacy of applying ML in security. 3.  ... 
arXiv:1611.03186v1 fatcat:hfvc5hhu7ze77lrnjufslcg6gm

A Survey on Journey of Topic Modeling Techniques from SVD to Deep Learning

Deepak Sharma, Bijendra Kumar, Satish Chand
2017 International Journal of Modern Education and Computer Science  
These techniques reveal the hidden thematic structure in a collection of documents and facilitate to build up new ways to browse, search and summarize large archive of texts.  ...  We have used the three hierarchical classification criteria's for classifying topic models that include LDA and non-LDA based, bag-of-words or sequence-of-words approach and unsupervised or supervised  ...  Most of the topics modeling techniques are fully unsupervised; there are few models used semi-supervised or supervised learning (S) for classify topics based on labeled data.  ... 
doi:10.5815/ijmecs.2017.07.06 fatcat:nadnmsoj4zdi7onlxivrne6gqm

Hyperspectral Image Classification Based on Sparse Superpixel Graph

Yifei Zhao, Fengqin Yan
2021 Remote Sensing  
To address these issues, this study proposes an efficient and effective semi-supervised spectral-spatial HSI classification method based on sparse superpixel graph (SSG).  ...  Hyperspectral image (HSI) classification is one of the major problems in the field of remote sensing.  ...  Special thanks are delivered to Professor Miloš Ilić for his kind help. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs13183592 fatcat:6hkvuw4eqrdwzhuyv4fsos3sea

A Markov semi-supervised clustering approach and its application in topological map extraction

Ming Liu, Francis Colas, Francois Pomerleau, Roland Siegwart
2012 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems  
We apply the designed model to a topological region extraction problem, where topological segmentation is constructed based on sparse human inputs (potentially provided by human experts).  ...  In this paper, we present a novel semi-supervised clustering approach based on Markov process. It deals with data which include abundant local constraints.  ...  SEMI-SUPERVISED CLUSTERING USING A MARKOV PROCESS Semi-supervised clustering is to use a small number of labeled data to aid the clustering of unlabeled data.  ... 
doi:10.1109/iros.2012.6385683 dblp:conf/iros/LiuCPS12 fatcat:7kucmm2rjffb5mhv37n5em33nm

Semi-supervised Learning [chapter]

Xiaojin Zhu
2017 Encyclopedia of Machine Learning and Data Mining  
Corduneanu and Jaakkola (2005) extend the work by formulating semi-supervised learning as a communication problem.  ...  Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers.  ... 
doi:10.1007/978-1-4899-7687-1_749 fatcat:a3pujecbsff5nlahnn36cmqdgi

Machine learning based hyperspectral image analysis: A survey [article]

Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
2019 arXiv   pre-print
We organize the methods by the image analysis task and by the type of machine learning algorithm, and present a two-way mapping between the image analysis tasks and the types of machine learning algorithms  ...  Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment  ...  [42] , a semi-supervised method that uses kernel matrix deformed by labeled and unlabeled data was proposed.  ... 
arXiv:1802.08701v2 fatcat:bfi6qkpx2bf6bowhyloj2duugu

Semi-supervised SRL System with Bayesian Inference [chapter]

Alejandra Lorenzo, Christophe Cerisara
2014 Lecture Notes in Computer Science  
We propose a new approach to perform semi-supervised training of Semantic Role Labeling models with very few amount of initial labeled data.  ...  The proposed approach combines in a novel way supervised and unsupervised training, by forcing the supervised classifier to overgenerate potential semantic candidates, and then letting unsupervised inference  ...  Acknowledgments This work has been partially funded by the French ANR project ContNomina.  ... 
doi:10.1007/978-3-642-54906-9_35 fatcat:ti4aqhm7tzf4vauoniwutm5s3e

Machine Learning Paradigms for Speech Recognition: An Overview

Li Deng, Xiao Li
2013 IEEE Transactions on Audio, Speech, and Language Processing  
Moreover, ML can and occasionally does use ASR as a large-scale, realistic application to rigorously test the effectiveness of a given technique, and to inspire new problems arising from the inherently  ...  On the other hand, even though ASR is available commercially for some applications, it is largely an unsolved problem-for almost all applications, the performance of ASR is not on par with human performance  ...  Appreciations also go to MSR for the encouragement and support of this "mentor-mentee project", to Helen Meng as the previous EIC for handling the white-paper reviews during 2009, and to the reviewers  ... 
doi:10.1109/tasl.2013.2244083 fatcat:fv4qulshnrh4fgzmzb45mkqwmq

An overview of multi-task learning

Yu Zhang, Qiang Yang
2017 National Science Review  
Then several different settings of MTL are introduced, including multi-task supervised learning, multi-task unsupervised learning, multi-task semi-supervised learning, multi-task active learning, multi-task  ...  In this paper, we give an overview of MTL by first giving a definition of MTL.  ...  MULTI-TASK SEMI-SUPERVISED LEARNING In many applications, data usually require a great deal of manual labor to label, making labeled data not so sufficient, but in many situations, unlabeled data are ample  ... 
doi:10.1093/nsr/nwx105 fatcat:7w67kng7ufbandtcneeniropny

A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning [article]

Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, Weixiong Zhang
2021 arXiv   pre-print
Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks.  ...  Community detection has been extensively studied in and broadly applied to many real-world network problems.  ...  For semi-supervised community detection, Yang et al.  ... 
arXiv:2101.01669v3 fatcat:p2lkjuslmzd6hc6kpum3sz5xwq

Data-driven Computational Social Science: A Survey

Jun Zhang, Wei Wang, Feng Xia, Yu-Ru Lin, Hanghang Tong
2020 Big Data Research  
With the aids of the advanced research techniques, various kinds of data from diverse areas can be acquired nowadays, and they can help us look into social problems with a new eye.  ...  In this paper, to the best of our knowledge, we present a survey on data-driven computational social science for the first time which primarily focuses on reviewing application domains involving human  ...  Known labels are used to propagate information through the graph in order to label all nodes.  ... 
doi:10.1016/j.bdr.2020.100145 fatcat:jazh5b3itfgmvh4pn37l4v5m7y

A Survey on Sentimental Analysis Approaches using Machine Learning Algorithms

Anith Ashok, Dr. Sandeep Monga
2022 International Journal for Research in Applied Science and Engineering Technology  
It is a way to evaluate written or spoken language to determine if the expression is favourable unfavourable, or neutral, and to what degree.  ...  It can further be improved by using hybrid models. Keywords: Time series forecasting, Sentiment Analysis, Hybrid Models  ...  Daume et al. (2010) [27] proposed a semi-supervised al-gorithm that uses labelled data in source and both labeled and unlabeled data in the target domain.  ... 
doi:10.22214/ijraset.2022.42005 fatcat:ss2f2lpznzcktakldpbete3evu

Anomalous Human Activity Recognition in Surveillance Videos

2019 International journal of recent technology and engineering  
Human-oriented problems such as security can be easily taken care of by detecting abnormal behavior.  ...  So, the paper has two parts that include adaptive video compression approaches of the surveillance videos and providing that compressed video as the input to detect anomalous human activity  ...  detection which requires only semi-supervised videos.  ... 
doi:10.35940/ijrte.b1064.0782s719 fatcat:352q6ou655dr7oj2jtk2rl6eca
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