Proposing a Localized Relevance Vector Machine for Pattern Classification Article Swipe
Farhood Rismanchian
,
Karim Rahimian
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1904.03688
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.1904.03688
Relevance vector machine (RVM) can be seen as a probabilistic version of support vector machines which is able to produce sparse solutions by linearly weighting a small number of basis functions instead using all of them. Regardless of a few merits of RVM such as giving probabilistic predictions and relax of parameter tuning, it has poor prediction for test instances that are far away from the relevance vectors. As a solution, we propose a new combination of RVM and k-nearest neighbor (k-NN) rule which resolves this issue with regionally dealing with every test instance. In our settings, we obtain the relevance vectors for each test instance in the local area given by k-NN rule. In this way, relevance vectors are closer and more relevant to the test instance which results in a more accurate model. This can be seen as a piece-wise learner which locally classifies test instances. The model is hence called localized relevance vector machine (LRVM). The LRVM is examined on several datasets of the University of California, Irvine (UCI) repository. Results supported by statistical tests indicate that the performance of LRVM is competitive as compared with a few state-of-the-art classifiers.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/1904.03688
- https://arxiv.org/pdf/1904.03688
- OA Status
- green
- Cited By
- 2
- References
- 31
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2932611961
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https://openalex.org/W2932611961Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.1904.03688Digital Object Identifier
- Title
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Proposing a Localized Relevance Vector Machine for Pattern ClassificationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2019Year of publication
- Publication date
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2019-04-07Full publication date if available
- Authors
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Farhood Rismanchian, Karim RahimianList of authors in order
- Landing page
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https://arxiv.org/abs/1904.03688Publisher landing page
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https://arxiv.org/pdf/1904.03688Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/1904.03688Direct OA link when available
- Concepts
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Relevance (law), Relevance vector machine, Support vector machine, Artificial intelligence, Computer science, Vector (molecular biology), Pattern recognition (psychology), Machine learning, Political science, Biology, Gene, Biochemistry, Law, Recombinant DNATop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2021: 1, 2019: 1Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.University | 167 |
| abstract_inverted_index.classifies | 145 |
| abstract_inverted_index.instances. | 147 |
| abstract_inverted_index.piece-wise | 141 |
| abstract_inverted_index.prediction | 56 |
| abstract_inverted_index.regionally | 88 |
| abstract_inverted_index.California, | 169 |
| abstract_inverted_index.combination | 75 |
| abstract_inverted_index.competitive | 185 |
| abstract_inverted_index.performance | 181 |
| abstract_inverted_index.predictions | 47 |
| abstract_inverted_index.repository. | 172 |
| abstract_inverted_index.statistical | 176 |
| abstract_inverted_index.classifiers. | 192 |
| abstract_inverted_index.probabilistic | 9, 46 |
| abstract_inverted_index.state-of-the-art | 191 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |