Theory and Methods sparse semi-supervised learning View Homepage


Ontology type: schema:MonetaryGrant     


Grant Info

YEARS

2010-2013

FUNDING AMOUNT

370000 CNY

ABSTRACT

Sparse semi-supervised learning considering the use of a small amount of labeled samples and some of the benefits unlabeled samples to predict the function expression with the new sample label prediction time is short, easy to expand into large-scale data, is increasingly subject to international peer attention. The project generated from two different ways sparsity, which uses the introduction of a specific norm standardized and insensitive loss function starting sparse semi-supervised learning theory and method of systematic study. Specific studies include: based on sparse data set of semi-supervised learning algorithm is sparse and sparse Laplacian support vector machine; convex function based on conjugated sparse semi-supervised learning dual representation and sparse multi-view support vector machine; large-scale data sequential training methods. By exploring and solving these problems, we can enhance the sparse semi-supervised learning method of comprehensive understanding, characterize the performance of the corresponding generalization machine learning, and for semi-supervised learning applications in large-scale data have a positive role in promoting. We will study in-depth statistical learning theory, convex optimization based content, drawing on relevant work of their predecessors, and strive in the sparse semi-supervised learning on this issue to achieve breakthrough results. More... »

URL

http://npd.nsfc.gov.cn/projectDetail.action?pid=61075005

Related SciGraph Publications

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/2008/2208", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "type": "DefinedTerm"
      }
    ], 
    "amount": {
      "currency": "CNY", 
      "type": "MonetaryAmount", 
      "value": "370000"
    }, 
    "description": "Sparse semi-supervised learning considering the use of a small amount of labeled samples and some of the benefits unlabeled samples to predict the function expression with the new sample label prediction time is short, easy to expand into large-scale data, is increasingly subject to international peer attention. The project generated from two different ways sparsity, which uses the introduction of a specific norm standardized and insensitive loss function starting sparse semi-supervised learning theory and method of systematic study. Specific studies include: based on sparse data set of semi-supervised learning algorithm is sparse and sparse Laplacian support vector machine; convex function based on conjugated sparse semi-supervised learning dual representation and sparse multi-view support vector machine; large-scale data sequential training methods. By exploring and solving these problems, we can enhance the sparse semi-supervised learning method of comprehensive understanding, characterize the performance of the corresponding generalization machine learning, and for semi-supervised learning applications in large-scale data have a positive role in promoting. We will study in-depth statistical learning theory, convex optimization based content, drawing on relevant work of their predecessors, and strive in the sparse semi-supervised learning on this issue to achieve breakthrough results.", 
    "endDate": "2013-12-30T00:00:00Z", 
    "funder": {
      "id": "https://www.grid.ac/institutes/grid.419696.5", 
      "type": "Organization"
    }, 
    "id": "sg:grant.4995493", 
    "identifier": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "4995493"
        ]
      }, 
      {
        "name": "nsfc_id", 
        "type": "PropertyValue", 
        "value": [
          "61075005"
        ]
      }
    ], 
    "inLanguage": [
      "zh"
    ], 
    "keywords": [
      "sparse semi", 
      "problem", 
      "performance", 
      "specific norms", 
      "sparse Laplacian support vector machine", 
      "introduction", 
      "depth", 
      "sparse multi-view support vector machine", 
      "systematic study", 
      "METHODS", 
      "learning applications", 
      "semi", 
      "learning", 
      "relevant work", 
      "issues", 
      "function expression", 
      "insensitive loss function", 
      "predecessors", 
      "use", 
      "comprehensive understanding", 
      "Sparse", 
      "small amount", 
      "international peer attention", 
      "samples", 
      "convex functions", 
      "learning method", 
      "sequential training methods", 
      "theory", 
      "unlabeled samples", 
      "new sample label prediction time", 
      "statistical learning theory", 
      "specific studies", 
      "sparse data sets", 
      "large-scale data", 
      "positive role", 
      "breakthrough results", 
      "convex optimization", 
      "content", 
      "benefits", 
      "learning algorithm", 
      "dual representation", 
      "corresponding generalization machine learning", 
      "project", 
      "different ways sparsity", 
      "learning theory"
    ], 
    "name": "Theory and Methods sparse semi-supervised learning", 
    "recipient": [
      {
        "id": "https://www.grid.ac/institutes/grid.22069.3f", 
        "type": "Organization"
      }, 
      {
        "affiliation": {
          "id": "https://www.grid.ac/institutes/grid.22069.3f", 
          "name": "East China Normal University", 
          "type": "Organization"
        }, 
        "familyName": "Sun", 
        "givenName": "Shi Liang", 
        "id": "sg:person.0776440313.65", 
        "type": "Person"
      }, 
      {
        "member": "sg:person.0776440313.65", 
        "roleName": "PI", 
        "type": "Role"
      }
    ], 
    "sameAs": [
      "https://app.dimensions.ai/details/grant/grant.4995493"
    ], 
    "sdDataset": "grants", 
    "sdDatePublished": "2019-03-07T12:44", 
    "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_6.xml.gz", 
    "startDate": "2010-12-31T00:00:00Z", 
    "type": "MonetaryGrant", 
    "url": "http://npd.nsfc.gov.cn/projectDetail.action?pid=61075005"
  }
]
 

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.4995493'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/grant.4995493'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/grant.4995493'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/grant.4995493'


 

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

89 TRIPLES      19 PREDICATES      67 URIs      59 LITERALS      5 BLANK NODES

Subject Predicate Object
1 sg:grant.4995493 schema:about anzsrc-for:2208
2 schema:amount N572dd8eeee8347cd9da6a1a75cfe971b
3 schema:description Sparse semi-supervised learning considering the use of a small amount of labeled samples and some of the benefits unlabeled samples to predict the function expression with the new sample label prediction time is short, easy to expand into large-scale data, is increasingly subject to international peer attention. The project generated from two different ways sparsity, which uses the introduction of a specific norm standardized and insensitive loss function starting sparse semi-supervised learning theory and method of systematic study. Specific studies include: based on sparse data set of semi-supervised learning algorithm is sparse and sparse Laplacian support vector machine; convex function based on conjugated sparse semi-supervised learning dual representation and sparse multi-view support vector machine; large-scale data sequential training methods. By exploring and solving these problems, we can enhance the sparse semi-supervised learning method of comprehensive understanding, characterize the performance of the corresponding generalization machine learning, and for semi-supervised learning applications in large-scale data have a positive role in promoting. We will study in-depth statistical learning theory, convex optimization based content, drawing on relevant work of their predecessors, and strive in the sparse semi-supervised learning on this issue to achieve breakthrough results.
4 schema:endDate 2013-12-30T00:00:00Z
5 schema:funder https://www.grid.ac/institutes/grid.419696.5
6 schema:identifier N18e690c963e54db1aca60b8a341a57cd
7 Nc8635cf7e1bf459c8a5e4a588fde4394
8 schema:inLanguage zh
9 schema:keywords METHODS
10 Sparse
11 benefits
12 breakthrough results
13 comprehensive understanding
14 content
15 convex functions
16 convex optimization
17 corresponding generalization machine learning
18 depth
19 different ways sparsity
20 dual representation
21 function expression
22 insensitive loss function
23 international peer attention
24 introduction
25 issues
26 large-scale data
27 learning
28 learning algorithm
29 learning applications
30 learning method
31 learning theory
32 new sample label prediction time
33 performance
34 positive role
35 predecessors
36 problem
37 project
38 relevant work
39 samples
40 semi
41 sequential training methods
42 small amount
43 sparse Laplacian support vector machine
44 sparse data sets
45 sparse multi-view support vector machine
46 sparse semi
47 specific norms
48 specific studies
49 statistical learning theory
50 systematic study
51 theory
52 unlabeled samples
53 use
54 schema:name Theory and Methods sparse semi-supervised learning
55 schema:recipient Nde1087e26266438b8c1f22f71a8d7fcd
56 sg:person.0776440313.65
57 https://www.grid.ac/institutes/grid.22069.3f
58 schema:sameAs https://app.dimensions.ai/details/grant/grant.4995493
59 schema:sdDatePublished 2019-03-07T12:44
60 schema:sdLicense https://scigraph.springernature.com/explorer/license/
61 schema:sdPublisher N29f679bebb4f4bbcb1a2b9abbfdfa8eb
62 schema:startDate 2010-12-31T00:00:00Z
63 schema:url http://npd.nsfc.gov.cn/projectDetail.action?pid=61075005
64 sgo:license sg:explorer/license/
65 sgo:sdDataset grants
66 rdf:type schema:MonetaryGrant
67 N18e690c963e54db1aca60b8a341a57cd schema:name nsfc_id
68 schema:value 61075005
69 rdf:type schema:PropertyValue
70 N29f679bebb4f4bbcb1a2b9abbfdfa8eb schema:name Springer Nature - SN SciGraph project
71 rdf:type schema:Organization
72 N572dd8eeee8347cd9da6a1a75cfe971b schema:currency CNY
73 schema:value 370000
74 rdf:type schema:MonetaryAmount
75 Nc8635cf7e1bf459c8a5e4a588fde4394 schema:name dimensions_id
76 schema:value 4995493
77 rdf:type schema:PropertyValue
78 Nde1087e26266438b8c1f22f71a8d7fcd schema:member sg:person.0776440313.65
79 schema:roleName PI
80 rdf:type schema:Role
81 anzsrc-for:2208 schema:inDefinedTermSet anzsrc-for:
82 rdf:type schema:DefinedTerm
83 sg:person.0776440313.65 schema:affiliation https://www.grid.ac/institutes/grid.22069.3f
84 schema:familyName Sun
85 schema:givenName Shi Liang
86 rdf:type schema:Person
87 https://www.grid.ac/institutes/grid.22069.3f schema:name East China Normal University
88 rdf:type schema:Organization
89 https://www.grid.ac/institutes/grid.419696.5 schema:Organization
 




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


...