The Internet Archive has a preservation copy of this work in our general collections.
The file type is application/pdf.
Filters
Kernel Topic Models
[article]
2011
arXiv
pre-print
Preserved Fulltext
This allows documents to be associated with elements of a Hilbert space, admitting kernel topic models (KTM), modelling temporal, spatial, hierarchical, social and other structure between documents. ...
The KTM can also be interpreted as a type of Gaussian process latent variable model, or as a topic model conditional on document features, uncovering links between earlier work in these areas. ...
Inference in the kernel topic model is cubic in the number of documents. ...
arXiv:1110.4713v1
fatcat:klwrebjkqvakxo6n24d7iccudi
Topic Model Kernel Classification With Probabilistically Reduced Features
2021
Journal of Data Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we describe the Topic Model Kernel (TMK), a topicbased kernel for Support Vector Machine classification on data being processed by probabilistic topic models. ...
Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. ...
performance. Inverser Multiquadric Kernel:
Figure 1 : 1 Probabilistic Topic Models. ...
doi:10.6339/jds.201507_13(3).0006
fatcat:5efq7yocmjfa3i3koqicn5vubm
Using Kernel Density Classifier with Topic Model and Cost Sensitive Learning for Automatic Text Categorization
2009
2009 10th International Conference on Document Analysis and Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
The experimental results confirm the effectiveness of the framework to utilize the features from the topic model for cost-sensitive categorization. 2009 10th International Conference on Document Analysis ...
This paper proposes a novel framework for automatic text categorization problem based on the kernel density classifier. ...
Topic Model In this paper, we use LSA to obtain a topic model. ...
doi:10.1109/icdar.2009.145
dblp:conf/icdar/MansjurWJ09
fatcat:cb4cnpszwnh6xb42yk2srd74y4
A Gaussian Kernel-based Spatiotemporal Fusion Model for Agricultural Remote Sensing Monitoring
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Index Terms-Spatiotemporal fusion; Gaussian kernel; time series; normalized difference vegetation index (NDVI) Ⅰ. ...
The experimental results show that GKSFM outperformed the comparative models in different proportions of cropland/non-cropland and different crop phenology. ...
In this study, a Gaussian kernel-based spatiotemporal fusion model (GKSFM) was proposed to fuse high-resolution NDVI (Landsat) and low-resolution NDVI (MODIS) during the crop growing season to produce ...
doi:10.1109/jstars.2021.3066055
fatcat:kooy6scex5dd7eeqex7lh5vyey
Development of Kernel-Driven Models With Fixed Hotspot Width Under a General Modeling Framework in the Thermal Infrared Domain
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
nine kernel-driven models with different coefficient requirements. ...
Under a general kernel-driven modeling framework proposed by Cao et al., by using three fixed-width hotspot kernels, and considering whether to combine two existing base shape kernels, this article proposed ...
ACKNOWLEDGMENT This authors would like to thank CESBIO for providing us the DART model 2 and Prof. B. Cao for providing instructions on the use of DART model. ...
doi:10.1109/jstars.2021.3110208
fatcat:r5u4a7przfhtpombchr4eu26gu
A KERNEL METHOD BASED ON TOPIC MODEL FOR VERY HIGH SPATIAL RESOLUTION (VHSR) REMOTE SENSING IMAGE CLASSIFICATION
2016
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
The new kernel method is based on spectral-spatial information and structure information as well, which is acquired from topic model, Latent Dirichlet Allocation model. ...
The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based ...
TOPIC MODEL Topic model is developed initially in text analysis domain for category and annotation. ...
doi:10.5194/isprsarchives-xli-b7-399-2016
fatcat:w3sec23hangalff33giqhk6if4
A KERNEL METHOD BASED ON TOPIC MODEL FOR VERY HIGH SPATIAL RESOLUTION (VHSR) REMOTE SENSING IMAGE CLASSIFICATION
2016
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
The new kernel method is based on spectral-spatial information and structure information as well, which is acquired from topic model, Latent Dirichlet Allocation model. ...
The result shows that the overall accuracy of the spectral- and structure-based kernel method is 80 %, which is higher than the spectral-based kernel method, as well as the spectral- and spatial-based ...
TOPIC MODEL Topic model is developed initially in text analysis domain for category and annotation. ...
doi:10.5194/isprs-archives-xli-b7-399-2016
fatcat:3zxlscbt7vd45pruah62bqzp6i
Improving kernel-driven BRDF model for capturing vegetation canopy reflectance with large leaf inclinations
2020
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Index Terms-Bidirectional reflectance distribution function (BRDF) /bidirectional reflectance factor (BRF), kernel-driven model, leaf inclination distribution, vegetation, volumetric scattering kernel. ...
Subsequently, we improved the RTLSR model into a four-parameter version (RTLSRV4p) with a new volumetric scattering kernel derived from the assumption of vertical leaf inclination. ...
Féret for providing the PROSAIL model code. The authors would also like to thank Dr. L.Y. Liu for sharing the valuable in situ canopy reflectance and SIF datasets. ...
doi:10.1109/jstars.2020.2987424
fatcat:7noloigfn5f6hfzgyog2ig7tmi
Gaussian Process Topic Models
[article]
2012
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
We introduce Gaussian Process Topic Models (GPTMs), a new family of topic models which can leverage a kernel among documents while extracting correlated topics. ...
Since GPTMs work with both a topic covariance matrix and a document kernel matrix, learning GPTMs involves a novel component-solving a suitable Sylvester equation capturing both topic and document dependencies ...
The final Figure 1 : Gaussian Process Topic Model embedding will be based on both the kernel as well as the structure of the documents as determined by the topic model. ...
arXiv:1203.3462v1
fatcat:56jsfvyd3bgi7nrunjoprt3fbu
Scalable Generalized Dynamic Topic Models
[article]
2018
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Dynamic topic models (DTMs) model the evolution of prevalent themes in literature, online media, and other forms of text over time. ...
These dynamical priors make inference much harder than in regular topic models, and also limit scalability. In this paper, we present several new results around DTMs. ...
If this prior is a Gaussian process, this leads to the kernel topic model (Hennig et al., 2012) or Gaussian process topic model (Agovic and Banerjee, 2012) . ...
arXiv:1803.07868v1
fatcat:v2irkbcbajahhkyjfh75pergay
A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain Knowledge from Wikipedia
2014
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2015; you can also visit the original URL.
The file type is application/pdf.
This paper proposes a composite kernel approach for dialog topic tracking to utilize various types of domain knowledge obtained from Wikipedia. ...
The experimental results show that our composite kernel approach can significantly improve the performances of topic tracking in mixed-initiative human-human dialogs. ...
While the history sequence kernel enhanced the coverage of the model to detect topic transitions, the domain context tree kernel contributed to produce more precise outputs. ...
doi:10.3115/v1/p14-2004
dblp:conf/acl/KimBL14
fatcat:xolq3pzpwjdvdprklzb6jrciqi
EARTH OBSERVATION IMAGE SEMANTICS: LATENT DIRICHLET ALLOCATION BASED INFORMATION DISCOVERY
2021
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
In the present study, Latent Dirichlet Allocation is employed for semantic discovery in RS images and a novel kernel-based Bag of Visual Words model is proposed for land cover mapping. ...
In the Kernel-based BOVW stage, the pixel-wise mid-level representation of the RS image is produced using the proposed kernel-based BOVW model. ...
However, further investigation is necessary in future studies to evaluate the performance of the kernel-based BOVW model as a pixel-wise alteration for patch-based BOVW model. ...
doi:10.5281/zenodo.6220982
fatcat:ad2dh7ybnjbanmynsmzm7pk3mu
Supporting systematic reviews using LDA-based document representations
2015
Systematic Reviews
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We apply Latent Dirichlet allocation (LDA), an unsupervised topic modelling approach, to automatically identify topics in a collection of studies. ...
Methods: We explore the use of topic modelling methods to derive a more informative representation of studies. ...
Figures 3 and 4 illustrate the results of using RBF and POLY kernel functions, respectively, in training BOW, topic-based models and TE-topic-based model on the youth development corpus. ...
doi:10.1186/s13643-015-0117-0
pmid:26612232
pmcid:PMC4662004
fatcat:3npam4hxlrfatimk56rh4iklri
Combining Thesaurus Knowledge and Probabilistic Topic Models
[chapter]
2017
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
If a general thesaurus, such as WordNet, is used, the thesaurus-based improvement of topic models can be achieved with excluding hyponymy relations in combined topic models. ...
In this paper we present the approach of introducing thesaurus knowledge into probabilistic topic models. ...
This study is supported by Russian Scientific Foundation in part concerning the combined approach uniting thesaurus information and probabilistic topic models (project N16-18-02074). ...
doi:10.1007/978-3-319-73013-4_6
fatcat:pz3p7qgwkffczci7fbphww4j7u
TPRM: A Topic-based Personalized Ranking Model for Web Search
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
In this paper, we propose a topic-based personalized ranking model (TPRM) that integrates user topical profile with pretrained contextualized term representations to tailor the general document ranking ...
Experiments on the real-world dataset demonstrate that TPRM outperforms state-of-the-art ad-hoc ranking models and personalized ranking models significantly. ...
Topic Number The number of topics is an important hyper-parameter in topic models. ...
arXiv:2108.06014v1
fatcat:wlnv744frvfsrexfocivyxbjzm
« Previous
Showing results 1 — 15 out of 147,222 results