Research on Nonparallel Support Vector Machine Optimization Based on Structure Learning View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2015-2015

FUNDING AMOUNT

30000 CNY

ABSTRACT

Support vector machine (SVM) has been recognized as one of the most effective learning methods in the machine learning community. As a new breakthrough, nonparallel support vector machine relaxes the universal requirement that the hyperplanes generated by SVM should be parallel, resulting in excellent at dealing with “xor” and “heteroscedastic noise” problems. Thus, methods of constructing nonparallel support vector machine has been extensively studied. However, compared with SVM, it still has many challenges, especially in its optimization theory and modeling. Based on our preliminary works, we study the nonparallel support vector machine from the following aspects: (1) we will propose a novel nonparallel support vector machine with maximum margin and unified metric, and further give its theoretical framework. (2) For the heteroscedastic distribution structure problem, we will construct a cluster-based structure nonparallel support vector machine via structure regularization penalty. (3) Combing with the over-relaxation and smoothing techniques, fast and sparse solving algorithms will be further designed for the above models. The goal of our project is provide the theory, methods and technical support for nonparallel support vector machine. More... »

URL

http://npd.nsfc.gov.cn/projectDetail.action?pid=11426202

Related SciGraph Publications

  • 2016-06. Least squares recursive projection twin support vector machine for multi-class classification in INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
  • 2015-09. Combined outputs framework for twin support vector machines in APPLIED INTELLIGENCE
  • JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2201", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/2208", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "type": "DefinedTerm"
          }
        ], 
        "amount": {
          "currency": "CNY", 
          "type": "MonetaryAmount", 
          "value": "30000"
        }, 
        "description": "Support vector machine (SVM) has been recognized as one of the most effective learning methods in the machine learning community. As a new breakthrough, nonparallel support vector machine relaxes the universal requirement that the hyperplanes generated by SVM should be parallel, resulting in excellent at dealing with \u201cxor\u201d and \u201cheteroscedastic noise\u201d problems. Thus, methods of constructing nonparallel support vector machine has been extensively studied. However, compared with SVM, it still has many challenges, especially in its optimization theory and modeling. Based on our preliminary works, we study the nonparallel support vector machine from the following aspects: (1) we will propose a novel nonparallel support vector machine with maximum margin and unified metric, and further give its theoretical framework. (2) For the heteroscedastic distribution structure problem, we will construct a cluster-based structure nonparallel support vector machine via structure regularization penalty. (3) Combing with the over-relaxation and smoothing techniques, fast and sparse solving algorithms will be further designed for the above models. The goal of our project is provide the theory, methods and technical support for nonparallel support vector machine.", 
        "endDate": "2015-12-31T00:00:00Z", 
        "funder": {
          "id": "https://www.grid.ac/institutes/grid.419696.5", 
          "type": "Organization"
        }, 
        "id": "sg:grant.6981209", 
        "identifier": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "6981209"
            ]
          }, 
          {
            "name": "nsfc_id", 
            "type": "PropertyValue", 
            "value": [
              "11426202"
            ]
          }
        ], 
        "inLanguage": [
          "zh"
        ], 
        "keywords": [
          "hyperplanes", 
          "effective learning method", 
          "theoretical framework", 
          "support vector machine", 
          "heteroscedastic distribution structure problem", 
          "project", 
          "technique", 
          "problem", 
          "theory", 
          "research", 
          "algorithms", 
          "machine", 
          "relaxation", 
          "optimization theory", 
          "METHODS", 
          "XOR", 
          "structure learning", 
          "universal requirement", 
          "many challenges", 
          "goal", 
          "nonparallel support vector machine", 
          "modeling", 
          "heteroscedastic noise", 
          "preliminary work", 
          "structure nonparallel support vector machine", 
          "structure regularization penalty", 
          "clusters", 
          "Nonparallel Support Vector Machine Optimization", 
          "new breakthroughs", 
          "aspects", 
          "community", 
          "technical support", 
          "above model", 
          "novel nonparallel support vector machine", 
          "maximum margin", 
          "unified metric"
        ], 
        "name": "Research on Nonparallel Support Vector Machine Optimization Based on Structure Learning", 
        "recipient": [
          {
            "id": "https://www.grid.ac/institutes/grid.469325.f", 
            "type": "Organization"
          }, 
          {
            "affiliation": {
              "id": "https://www.grid.ac/institutes/grid.469325.f", 
              "name": "ZHEJIANG UNIVERSITY OF TECHNOLOGY", 
              "type": "Organization"
            }, 
            "familyName": "Chen", 
            "givenName": "Wei Jie", 
            "id": "sg:person.015523541760.32", 
            "type": "Person"
          }, 
          {
            "member": "sg:person.015523541760.32", 
            "roleName": "PI", 
            "type": "Role"
          }
        ], 
        "sameAs": [
          "https://app.dimensions.ai/details/grant/grant.6981209"
        ], 
        "sdDataset": "grants", 
        "sdDatePublished": "2019-03-07T12:40", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com.uberresearch.data.processor/core_data/20181219_192338/projects/base/nsfc_projects_1.xml.gz", 
        "startDate": "2015-01-01T00:00:00Z", 
        "type": "MonetaryGrant", 
        "url": "http://npd.nsfc.gov.cn/projectDetail.action?pid=11426202"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/grant.6981209'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.6981209'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.6981209'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.6981209'


     

    This table displays all metadata directly associated to this object as RDF triples.

    83 TRIPLES      19 PREDICATES      59 URIs      50 LITERALS      5 BLANK NODES

    Subject Predicate Object
    1 sg:grant.6981209 schema:about anzsrc-for:2201
    2 anzsrc-for:2208
    3 schema:amount N3703bf10b9de486fa4919f5fc5cce35c
    4 schema:description Support vector machine (SVM) has been recognized as one of the most effective learning methods in the machine learning community. As a new breakthrough, nonparallel support vector machine relaxes the universal requirement that the hyperplanes generated by SVM should be parallel, resulting in excellent at dealing with “xor” and “heteroscedastic noise” problems. Thus, methods of constructing nonparallel support vector machine has been extensively studied. However, compared with SVM, it still has many challenges, especially in its optimization theory and modeling. Based on our preliminary works, we study the nonparallel support vector machine from the following aspects: (1) we will propose a novel nonparallel support vector machine with maximum margin and unified metric, and further give its theoretical framework. (2) For the heteroscedastic distribution structure problem, we will construct a cluster-based structure nonparallel support vector machine via structure regularization penalty. (3) Combing with the over-relaxation and smoothing techniques, fast and sparse solving algorithms will be further designed for the above models. The goal of our project is provide the theory, methods and technical support for nonparallel support vector machine.
    5 schema:endDate 2015-12-31T00:00:00Z
    6 schema:funder https://www.grid.ac/institutes/grid.419696.5
    7 schema:identifier N76917a1b23f24383bc3946ba60ca2726
    8 Nf88dcdf305f549dfae780dd5f9b91372
    9 schema:inLanguage zh
    10 schema:keywords METHODS
    11 Nonparallel Support Vector Machine Optimization
    12 XOR
    13 above model
    14 algorithms
    15 aspects
    16 clusters
    17 community
    18 effective learning method
    19 goal
    20 heteroscedastic distribution structure problem
    21 heteroscedastic noise
    22 hyperplanes
    23 machine
    24 many challenges
    25 maximum margin
    26 modeling
    27 new breakthroughs
    28 nonparallel support vector machine
    29 novel nonparallel support vector machine
    30 optimization theory
    31 preliminary work
    32 problem
    33 project
    34 relaxation
    35 research
    36 structure learning
    37 structure nonparallel support vector machine
    38 structure regularization penalty
    39 support vector machine
    40 technical support
    41 technique
    42 theoretical framework
    43 theory
    44 unified metric
    45 universal requirement
    46 schema:name Research on Nonparallel Support Vector Machine Optimization Based on Structure Learning
    47 schema:recipient N4e5c159009c443ccb7ed9098d3f5305a
    48 sg:person.015523541760.32
    49 https://www.grid.ac/institutes/grid.469325.f
    50 schema:sameAs https://app.dimensions.ai/details/grant/grant.6981209
    51 schema:sdDatePublished 2019-03-07T12:40
    52 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    53 schema:sdPublisher N0e06026c26e944cc9f526816ba5cf794
    54 schema:startDate 2015-01-01T00:00:00Z
    55 schema:url http://npd.nsfc.gov.cn/projectDetail.action?pid=11426202
    56 sgo:license sg:explorer/license/
    57 sgo:sdDataset grants
    58 rdf:type schema:MonetaryGrant
    59 N0e06026c26e944cc9f526816ba5cf794 schema:name Springer Nature - SN SciGraph project
    60 rdf:type schema:Organization
    61 N3703bf10b9de486fa4919f5fc5cce35c schema:currency CNY
    62 schema:value 30000
    63 rdf:type schema:MonetaryAmount
    64 N4e5c159009c443ccb7ed9098d3f5305a schema:member sg:person.015523541760.32
    65 schema:roleName PI
    66 rdf:type schema:Role
    67 N76917a1b23f24383bc3946ba60ca2726 schema:name nsfc_id
    68 schema:value 11426202
    69 rdf:type schema:PropertyValue
    70 Nf88dcdf305f549dfae780dd5f9b91372 schema:name dimensions_id
    71 schema:value 6981209
    72 rdf:type schema:PropertyValue
    73 anzsrc-for:2201 schema:inDefinedTermSet anzsrc-for:
    74 rdf:type schema:DefinedTerm
    75 anzsrc-for:2208 schema:inDefinedTermSet anzsrc-for:
    76 rdf:type schema:DefinedTerm
    77 sg:person.015523541760.32 schema:affiliation https://www.grid.ac/institutes/grid.469325.f
    78 schema:familyName Chen
    79 schema:givenName Wei Jie
    80 rdf:type schema:Person
    81 https://www.grid.ac/institutes/grid.419696.5 schema:Organization
    82 https://www.grid.ac/institutes/grid.469325.f schema:name ZHEJIANG UNIVERSITY OF TECHNOLOGY
    83 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...