Get quick answers to your questions about the article from our AI researcher chatbot
{'id': 'https://openalex.org/W2130792056', 'doi': 'https://doi.org/10.1109/cvpr.2014.547', 'title': 'Simultaneous Twin Kernel Learning Using Polynomial Transformations for Structured Prediction', 'display_name': 'Simultaneous Twin Kernel Learning Using Polynomial Transformations for Structured Prediction', 'publication_year': 2014, 'publication_date': '2014-06-01', 'ids': {'openalex': 'https://openalex.org/W2130792056', 'doi': 'https://doi.org/10.1109/cvpr.2014.547', 'mag': '2130792056'}, 'language': 'en', 'primary_location': {'is_oa': False, 'landing_page_url': 'https://doi.org/10.1109/cvpr.2014.547', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4363607795', 'display_name': '2009 IEEE Conference on Computer Vision and Pattern Recognition', 'issn_l': None, 'issn': None, 'is_oa': False, 'is_in_doaj': False, 'is_core': False, 'host_organization': None, 'host_organization_name': None, 'host_organization_lineage': [], 'host_organization_lineage_names': [], 'type': 'conference'}, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}, 'type': 'article', 'type_crossref': 'proceedings-article', 'indexed_in': ['crossref'], 'open_access': {'is_oa': True, 'oa_status': 'green', 'oa_url': 'https://scholarship.libraries.rutgers.edu/esploro/fulltext/acceptedManuscript/Simultaneous-Twin-Kernel-Learning-Using-Polynomial/991031549923804646?repId=12643382360004646&mId=13643549570004646&institution=01RUT_INST', 'any_repository_has_fulltext': True}, 'authorships': [{'author_position': 'first', 'author': {'id': 'https://openalex.org/A5091661411', 'display_name': 'Chetan Tonde', 'orcid': None}, 'institutions': [{'id': 'https://openalex.org/I102322142', 'display_name': 'Rutgers, The State University of New Jersey', 'ror': 'https://ror.org/05vt9qd57', 'country_code': 'US', 'type': 'education', 'lineage': ['https://openalex.org/I102322142']}], 'countries': ['US'], 'is_corresponding': False, 'raw_author_name': 'Chetan Tonde', 'raw_affiliation_strings': ['Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, New Brunswick, NJ, USA'], 'affiliations': [{'raw_affiliation_string': 'Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, New Brunswick, NJ, USA', 'institution_ids': ['https://openalex.org/I102322142']}]}, {'author_position': 'last', 'author': {'id': 'https://openalex.org/A5039640321', 'display_name': 'Ahmed Elgammal', 'orcid': 'https://orcid.org/0000-0003-2761-4822'}, 'institutions': [{'id': 'https://openalex.org/I102322142', 'display_name': 'Rutgers, The State University of New Jersey', 'ror': 'https://ror.org/05vt9qd57', 'country_code': 'US', 'type': 'education', 'lineage': ['https://openalex.org/I102322142']}], 'countries': ['US'], 'is_corresponding': False, 'raw_author_name': 'Ahmed Elgammal', 'raw_affiliation_strings': ['Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, New Brunswick, NJ, USA'], 'affiliations': [{'raw_affiliation_string': 'Dept. of Comput. Sci., Rutgers, State Univ. of New Jersey, New Brunswick, NJ, USA', 'institution_ids': ['https://openalex.org/I102322142']}]}], 'institution_assertions': [], 'countries_distinct_count': 1, 'institutions_distinct_count': 1, 'corresponding_author_ids': [], 'corresponding_institution_ids': [], 'apc_list': None, 'apc_paid': None, 'fwci': 0.146, 'has_fulltext': True, 'fulltext_origin': 'ngrams', 'cited_by_count': 1, 'citation_normalized_percentile': {'value': 0.198715, 'is_in_top_1_percent': False, 'is_in_top_10_percent': False}, 'cited_by_percentile_year': {'min': 66, 'max': 73}, 'biblio': {'volume': None, 'issue': None, 'first_page': '995', 'last_page': '1002'}, 'is_retracted': False, 'is_paratext': False, 'primary_topic': {'id': 'https://openalex.org/T12814', 'display_name': 'Gaussian Processes and Bayesian Inference', 'score': 0.9933, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}, 'topics': [{'id': 'https://openalex.org/T12814', 'display_name': 'Gaussian Processes and Bayesian Inference', 'score': 0.9933, 'subfield': {'id': 'https://openalex.org/subfields/1702', 'display_name': 'Artificial Intelligence'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}, {'id': 'https://openalex.org/T10812', 'display_name': 'Human Pose and Action Recognition', 'score': 0.9623, 'subfield': {'id': 'https://openalex.org/subfields/1707', 'display_name': 'Computer Vision and Pattern Recognition'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}, {'id': 'https://openalex.org/T10331', 'display_name': 'Video Surveillance and Tracking Methods', 'score': 0.955, 'subfield': {'id': 'https://openalex.org/subfields/1707', 'display_name': 'Computer Vision and Pattern Recognition'}, 'field': {'id': 'https://openalex.org/fields/17', 'display_name': 'Computer Science'}, 'domain': {'id': 'https://openalex.org/domains/3', 'display_name': 'Physical Sciences'}}], 'keywords': [{'id': 'https://openalex.org/keywords/tree-kernel', 'display_name': 'Tree kernel', 'score': 0.8161783}, {'id': 'https://openalex.org/keywords/kernel', 'display_name': 'Kernel (algebra)', 'score': 0.71314824}, {'id': 'https://openalex.org/keywords/string-kernel', 'display_name': 'String kernel', 'score': 0.7009508}, {'id': 'https://openalex.org/keywords/graph-kernel', 'display_name': 'Graph kernel', 'score': 0.49492544}], 'concepts': [{'id': 'https://openalex.org/C160446489', 'wikidata': 'https://www.wikidata.org/wiki/Q7226642', 'display_name': 'Polynomial kernel', 'level': 4, 'score': 0.8601567}, {'id': 'https://openalex.org/C140417398', 'wikidata': 'https://www.wikidata.org/wiki/Q16933942', 'display_name': 'Tree kernel', 'level': 5, 'score': 0.8161783}, {'id': 'https://openalex.org/C134517425', 'wikidata': 'https://www.wikidata.org/wiki/Q16000131', 'display_name': 'Kernel embedding of distributions', 'level': 4, 'score': 0.7177194}, {'id': 'https://openalex.org/C74193536', 'wikidata': 'https://www.wikidata.org/wiki/Q574844', 'display_name': 'Kernel (algebra)', 'level': 2, 'score': 0.71314824}, {'id': 'https://openalex.org/C80884492', 'wikidata': 'https://www.wikidata.org/wiki/Q3345678', 'display_name': 'Reproducing kernel Hilbert space', 'level': 3, 'score': 0.7046303}, {'id': 'https://openalex.org/C55851704', 'wikidata': 'https://www.wikidata.org/wiki/Q7623983', 'display_name': 'String kernel', 'level': 5, 'score': 0.7009508}, {'id': 'https://openalex.org/C75866337', 'wikidata': 'https://www.wikidata.org/wiki/Q7280263', 'display_name': 'Radial basis function kernel', 'level': 4, 'score': 0.6803942}, {'id': 'https://openalex.org/C122280245', 'wikidata': 'https://www.wikidata.org/wiki/Q620622', 'display_name': 'Kernel method', 'level': 3, 'score': 0.61925364}, {'id': 'https://openalex.org/C41008148', 'wikidata': 'https://www.wikidata.org/wiki/Q21198', 'display_name': 'Computer science', 'level': 0, 'score': 0.55937606}, {'id': 'https://openalex.org/C154945302', 'wikidata': 'https://www.wikidata.org/wiki/Q11660', 'display_name': 'Artificial intelligence', 'level': 1, 'score': 0.53002864}, {'id': 'https://openalex.org/C100595998', 'wikidata': 'https://www.wikidata.org/wiki/Q11731931', 'display_name': 'Graph kernel', 'level': 5, 'score': 0.49492544}, {'id': 'https://openalex.org/C2776214188', 'wikidata': 'https://www.wikidata.org/wiki/Q408386', 'display_name': 'Inference', 'level': 2, 'score': 0.4311229}, {'id': 'https://openalex.org/C7218915', 'wikidata': 'https://www.wikidata.org/wiki/Q1054475', 'display_name': 'Gaussian function', 'level': 3, 'score': 0.42992833}, {'id': 'https://openalex.org/C182335926', 'wikidata': 'https://www.wikidata.org/wiki/Q17093020', 'display_name': 'Kernel principal component analysis', 'level': 4, 'score': 0.42579502}, {'id': 'https://openalex.org/C195699287', 'wikidata': 'https://www.wikidata.org/wiki/Q7915722', 'display_name': 'Variable kernel density estimation', 'level': 4, 'score': 0.42350903}, {'id': 'https://openalex.org/C33923547', 'wikidata': 'https://www.wikidata.org/wiki/Q395', 'display_name': 'Mathematics', 'level': 0, 'score': 0.38696817}, {'id': 'https://openalex.org/C11413529', 'wikidata': 'https://www.wikidata.org/wiki/Q8366', 'display_name': 'Algorithm', 'level': 1, 'score': 0.3773708}, {'id': 'https://openalex.org/C119857082', 'wikidata': 'https://www.wikidata.org/wiki/Q2539', 'display_name': 'Machine learning', 'level': 1, 'score': 0.3646368}, {'id': 'https://openalex.org/C153180895', 'wikidata': 'https://www.wikidata.org/wiki/Q7148389', 'display_name': 'Pattern recognition (psychology)', 'level': 2, 'score': 0.34480917}, {'id': 'https://openalex.org/C12267149', 'wikidata': 'https://www.wikidata.org/wiki/Q282453', 'display_name': 'Support vector machine', 'level': 2, 'score': 0.32828778}, {'id': 'https://openalex.org/C62799726', 'wikidata': 'https://www.wikidata.org/wiki/Q190056', 'display_name': 'Hilbert space', 'level': 2, 'score': 0.32355687}, {'id': 'https://openalex.org/C163716315', 'wikidata': 'https://www.wikidata.org/wiki/Q901177', 'display_name': 'Gaussian', 'level': 2, 'score': 0.31598246}, {'id': 'https://openalex.org/C118615104', 'wikidata': 'https://www.wikidata.org/wiki/Q121416', 'display_name': 'Discrete mathematics', 'level': 1, 'score': 0.1275717}, {'id': 'https://openalex.org/C134306372', 'wikidata': 'https://www.wikidata.org/wiki/Q7754', 'display_name': 'Mathematical analysis', 'level': 1, 'score': 0.0}, {'id': 'https://openalex.org/C121332964', 'wikidata': 'https://www.wikidata.org/wiki/Q413', 'display_name': 'Physics', 'level': 0, 'score': 0.0}, {'id': 'https://openalex.org/C62520636', 'wikidata': 'https://www.wikidata.org/wiki/Q944', 'display_name': 'Quantum mechanics', 'level': 1, 'score': 0.0}], 'mesh': [], 'locations_count': 2, 'locations': [{'is_oa': False, 'landing_page_url': 'https://doi.org/10.1109/cvpr.2014.547', 'pdf_url': None, 'source': {'id': 'https://openalex.org/S4363607795', 'display_name': '2009 IEEE Conference on Computer Vision and Pattern Recognition', 'issn_l': None, 'issn': None, 'is_oa': False, 'is_in_doaj': False, 'is_core': False, 'host_organization': None, 'host_organization_name': None, 'host_organization_lineage': [], 'host_organization_lineage_names': [], 'type': 'conference'}, 'license': None, 'license_id': None, 'version': None, 'is_accepted': False, 'is_published': False}, {'is_oa': True, 'landing_page_url': 'https://scholarship.libraries.rutgers.edu/esploro/outputs/acceptedManuscript/Simultaneous-Twin-Kernel-Learning-Using-Polynomial/991031549923804646', 'pdf_url': 'https://scholarship.libraries.rutgers.edu/esploro/fulltext/acceptedManuscript/Simultaneous-Twin-Kernel-Learning-Using-Polynomial/991031549923804646?repId=12643382360004646&mId=13643549570004646&institution=01RUT_INST', 'source': None, 'license': 'other-oa', 'license_id': 'https://openalex.org/licenses/other-oa', 'version': 'acceptedVersion', 'is_accepted': True, 'is_published': False}], 'best_oa_location': {'is_oa': True, 'landing_page_url': 'https://scholarship.libraries.rutgers.edu/esploro/outputs/acceptedManuscript/Simultaneous-Twin-Kernel-Learning-Using-Polynomial/991031549923804646', 'pdf_url': 'https://scholarship.libraries.rutgers.edu/esploro/fulltext/acceptedManuscript/Simultaneous-Twin-Kernel-Learning-Using-Polynomial/991031549923804646?repId=12643382360004646&mId=13643549570004646&institution=01RUT_INST', 'source': None, 'license': 'other-oa', 'license_id': 'https://openalex.org/licenses/other-oa', 'version': 'acceptedVersion', 'is_accepted': True, 'is_published': False}, 'sustainable_development_goals': [], 'grants': [], 'datasets': [], 'versions': [], 'referenced_works_count': 35, 'referenced_works': ['https://openalex.org/W1638081485', 'https://openalex.org/W1746819321', 'https://openalex.org/W1920328734', 'https://openalex.org/W2034869648', 'https://openalex.org/W205396393', 'https://openalex.org/W2096765209', 'https://openalex.org/W2098941887', 'https://openalex.org/W2102181413', 'https://openalex.org/W2103194807', 'https://openalex.org/W2105842272', 'https://openalex.org/W2108825216', 'https://openalex.org/W2115003579', 'https://openalex.org/W2117496083', 'https://openalex.org/W2121033924', 'https://openalex.org/W2124101779', 'https://openalex.org/W2136064009', 'https://openalex.org/W2139710299', 'https://openalex.org/W2141224867', 'https://openalex.org/W2142387771', 'https://openalex.org/W2144752499', 'https://openalex.org/W2145295623', 'https://openalex.org/W2145544165', 'https://openalex.org/W2151598303', 'https://openalex.org/W2161969291', 'https://openalex.org/W2162724919', 'https://openalex.org/W2168029744', 'https://openalex.org/W2169738563', 'https://openalex.org/W2170356051', 'https://openalex.org/W2186534467', 'https://openalex.org/W2950952738', 'https://openalex.org/W3101749733', 'https://openalex.org/W4206733017', 'https://openalex.org/W4211049957', 'https://openalex.org/W4246066915', 'https://openalex.org/W4285719527'], 'related_works': ['https://openalex.org/W3100948281', 'https://openalex.org/W3099811568', 'https://openalex.org/W3081470858', 'https://openalex.org/W3013206934', 'https://openalex.org/W2574115973', 'https://openalex.org/W2147750455', 'https://openalex.org/W2130792056', 'https://openalex.org/W2090782076', 'https://openalex.org/W1983263273', 'https://openalex.org/W1590832708'], 'abstract_inverted_index': {'Many': [0], 'learning': [1, 63, 135, 150, 174, 184], 'problems': [2], 'in': [3, 103, 206], 'computer': [4], 'vision': [5], 'can': [6, 164], 'be': [7], 'posed': [8], 'as': [9, 23, 39, 175], 'structured': [10, 20], 'prediction': [11], 'problems,': [12], 'where': [13], 'the': [14, 92, 97, 120, 147], 'input': [15, 121, 141], 'and': [16, 51, 64, 131, 142, 154, 199], 'output': [17, 143], 'instances': [18, 83], 'are': [19], 'objects': [21], 'such': [22, 38], 'trees,': [24], 'graphs': [25], 'or': [26, 34], 'strings': [27], 'rather': [28], 'than,': [29], 'single': [30], 'labels': [31], '{+1,': [32], '-1}': [33], 'scalars.': [35], 'Kernel': [36, 54, 160], 'methods': [37, 109], 'Structured': [40, 48], 'Support': [41], 'Vector': [42], 'Machines,': [43], 'Twin': [44, 159, 188], 'Gaussian': [45, 49, 189], 'Processes': [46], '(TGP),': [47], 'Processes,': [50], 'vector-valued': [52], 'Reproducing': [53], 'Hilbert': [55], 'Spaces': [56], '(RKHS),': [57], 'offer': [58], 'powerful': [59], 'ways': [60], 'to': [61, 75], 'perform': [62], 'inference': [65], 'over': [66, 84], 'these': [67, 85], 'domains.': [68, 87, 144], 'Positive': [69], 'definite': [70], 'kernel': [71, 93, 107, 136, 152, 169, 173], 'functions': [72, 137], 'allow': [73], 'us': [74], 'quantitatively': [76], 'capture': [77], 'similarity': [78], 'between': [79], 'a': [80, 129, 176], 'pair': [81], 'of': [82, 91, 149, 187, 208], 'arbitrary': [86], 'A': [88], 'poor': [89, 104], 'choice': [90], 'function,': [94], 'which': [95], 'decides': [96], 'RKHS': [98], 'feature': [99], 'space,': [100], 'often': [101], 'results': [102], 'performance.': [105], 'Automatic': [106], 'selection': [108], 'have': [110, 114], 'been': [111], 'developed,': [112], 'but': [113, 167], 'focused': [115], 'only': [116], 'on': [117, 119, 139, 197], 'kernels': [118, 186, 196], 'domain': [122], "(i.e.'one-way').": [123], 'In': [124], 'this': [125, 156, 181], 'work,': [126], 'we': [127], 'propose': [128], 'novel': [130], 'efficient': [132], 'algorithm': [133], 'for': [134, 183], 'simultaneously,': [138], 'both': [140], 'We': [145, 179], 'introduce': [146], 'idea': [148], 'polynomial': [151], 'transformations,': [153], 'call': [155], 'method': [157], 'Simultaneous': [158], 'Learning': [161], '(STKL).': [162], 'STKL': [163], 'learn': [165], 'arbitrary,': [166], 'continuous': [168], 'functions,': [170], 'including': [171], "'one-way'": [172], 'special': [177], 'case.': [178], 'formulate': [180], 'problem': [182], 'covariances': [185], 'Processes.': [190], 'Our': [191], 'experimental': [192], 'evaluation': [193], 'using': [194], 'learned': [195], 'synthetic': [198], 'several': [200], 'real-world': [201], 'datasets': [202], 'demonstrate': [203], 'consistent': [204], 'improvement': [205], 'performance': [207], "TGP's.": [209]}, 'cited_by_api_url': 'https://api.openalex.org/works?filter=cites:W2130792056', 'counts_by_year': [{'year': 2015, 'cited_by_count': 1}], 'updated_date': '2024-12-08T00:16:22.492664', 'created_date': '2016-06-24'}