{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/method/svm/papers/13","list_of":"/method/svm","method":"SVM","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"date (newest first), then slug","page":13,"pages_in_order":19,"rows_per_page":100,"rows":[1201,1300],"of":1823,"counts":{"archive_papers_tagged":1823,"with_a_code_link":328,"where_syntology_ran_a_sample":28,"not_listed_spam_title":0,"listed":1823,"listed_where_code_ran":28,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/method/svm","prev":"/method/svm/papers/12","next":"/method/svm/papers/14","papers":[{"paper":null,"slug":"compact-deep-neural-networks-for","title":"Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals","date":"2018-06-22","arxiv_id":"1806.08641","n_code_links":0,"syntology":null},{"paper":"/paper/injecting-relational-structural","slug":"injecting-relational-structural","title":"Injecting Relational Structural Representation in Neural Networks for Question Similarity","date":"2018-06-20","arxiv_id":"1806.08009","n_code_links":1,"syntology":null},{"paper":null,"slug":"a-new-cold-feature-based-handwriting-analysis","title":"A New COLD Feature based Handwriting Analysis for Ethnicity/Nationality Identification","date":"2018-06-19","arxiv_id":"1806.07072","n_code_links":0,"syntology":null},{"paper":"/paper/an-ensemble-of-transfer-semi-supervised-and","slug":"an-ensemble-of-transfer-semi-supervised-and","title":"An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification","date":"2018-06-18","arxiv_id":"1806.06506","n_code_links":1,"syntology":null},{"paper":null,"slug":"overlapping-clustering-models-and-one-class","title":"Overlapping Clustering Models, and One (class) SVM to Bind Them All","date":"2018-06-18","arxiv_id":"1806.06945","n_code_links":0,"syntology":null},{"paper":null,"slug":"pac-bayes-bounds-for-stable-algorithms-with","title":"PAC-Bayes bounds for stable algorithms with instance-dependent priors","date":"2018-06-18","arxiv_id":"1806.06827","n_code_links":0,"syntology":null},{"paper":"/paper/a-novel-hybrid-machine-learning-model-for","slug":"a-novel-hybrid-machine-learning-model-for","title":"A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases","date":"2018-06-17","arxiv_id":"1806.06423","n_code_links":1,"syntology":null},{"paper":null,"slug":"supervised-fuzzy-partitioning","title":"Supervised Fuzzy Partitioning","date":"2018-06-15","arxiv_id":"1806.06124","n_code_links":0,"syntology":null},{"paper":null,"slug":"investigating-the-impact-of-cnn-depth-on","title":"Investigating the Impact of CNN Depth on Neonatal Seizure Detection Performance","date":"2018-06-08","arxiv_id":"1806.03044","n_code_links":0,"syntology":null},{"paper":null,"slug":"machine-learning-cicy-threefolds","title":"Machine Learning CICY Threefolds","date":"2018-06-08","arxiv_id":"1806.03121","n_code_links":0,"syntology":null},{"paper":"/paper/supportnet-solving-catastrophic-forgetting-in","slug":"supportnet-solving-catastrophic-forgetting-in","title":"SupportNet: solving catastrophic forgetting in class incremental learning with support data","date":"2018-06-08","arxiv_id":"1806.02942","n_code_links":1,"syntology":null},{"paper":null,"slug":"semiparametric-classification-of-forest","title":"Beyond Trees: Classification with Sparse Pairwise Dependencies","date":"2018-06-06","arxiv_id":"1806.01993","n_code_links":0,"syntology":null},{"paper":null,"slug":"alb-at-semeval-2018-task-10-a-system-for","title":"ALB at SemEval-2018 Task 10: A System for Capturing Discriminative Attributes","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"amritanlp-at-semeval-2018-task-10-capturing","title":"AmritaNLP at SemEval-2018 Task 10: Capturing discriminative attributes using convolution neural network over global vector representation.","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"claire-at-semeval-2018-task-7-classification","title":"ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"coding-kendalls-shape-trajectories-for-3d","title":"Coding Kendall's Shape Trajectories for 3D Action Recognition","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"enabling-deep-learning-of-emotion-with-first","title":"Enabling Deep Learning of Emotion With First-Person Seed Expressions","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"hatching-chick-at-semeval-2018-task-2","title":"Hatching Chick at SemEval-2018 Task 2: Multilingual Emoji Prediction","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/large-margin-classification-in-hyperbolic","slug":"large-margin-classification-in-hyperbolic","title":"Large-Margin Classification in Hyperbolic Space","date":"2018-06-01","arxiv_id":"1806.00437","n_code_links":2,"syntology":null},{"paper":null,"slug":"pickleteam-at-semeval-2018-task-2-english-and","title":"PickleTeam! at SemEval-2018 Task 2: English and Spanish Emoji Prediction from Tweets","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"ta14bingen-oslo-at-semeval-2018-task-2-svms","title":"T\\\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"takelab-at-semeval-2018-task-7-combining","title":"TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"uiuc-at-semeval-2018-task-1-recognizing","title":"UIUC at SemEval-2018 Task 1: Recognizing Affect with Ensemble Models","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"umd-at-semeval-2018-task-10-can-word","title":"UMD at SemEval-2018 Task 10: Can Word Embeddings Capture Discriminative Attributes?","date":"2018-06-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/anonymous-walk-embeddings","slug":"anonymous-walk-embeddings","title":"Anonymous Walk Embeddings","date":"2018-05-30","arxiv_id":"1805.11921","n_code_links":2,"syntology":{"ran":2,"of":3,"n_ran_checked":1,"n_instrument":1,"unverified":1,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","official":{"repos":["nd7141/AWE"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"classification-with-imperfect-training-labels","title":"Classification with imperfect training labels","date":"2018-05-29","arxiv_id":"1805.11505","n_code_links":0,"syntology":null},{"paper":"/paper/part-based-visual-tracking-via-structural","slug":"part-based-visual-tracking-via-structural","title":"Part-based Visual Tracking via Structural Support Correlation Filter","date":"2018-05-25","arxiv_id":"1805.09971","n_code_links":1,"syntology":null},{"paper":null,"slug":"dnn-or-k-nn-that-is-the-generalize-vs","title":"DNN or k-NN: That is the Generalize vs. Memorize Question","date":"2018-05-17","arxiv_id":"1805.06822","n_code_links":0,"syntology":null},{"paper":null,"slug":"towards-explaining-anomalies-a-deep-taylor","title":"Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models","date":"2018-05-16","arxiv_id":"1805.06230","n_code_links":0,"syntology":null},{"paper":"/paper/detecting-decision-ambiguity-from-facial","slug":"detecting-decision-ambiguity-from-facial","title":"Detecting Decision Ambiguity from Facial Images","date":"2018-05-15","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"slug":"hybrid-adaptive-fuzzy-extreme-learning","title":"Hybrid Adaptive Fuzzy Extreme Learning Machine for text classification","date":"2018-05-10","arxiv_id":"1805.06524","n_code_links":0,"syntology":null},{"paper":null,"slug":"region-based-classification-of-polsar-data","title":"Region-Based Classification of PolSAR Data Using Radial Basis Kernel Functions With Stochastic Distances","date":"2018-05-07","arxiv_id":"1805.07438","n_code_links":0,"syntology":null},{"paper":null,"slug":"examining-the-use-of-neural-networks-for","title":"Examining the Use of Neural Networks for Feature Extraction: A Comparative Analysis using Deep Learning, Support Vector Machines, and K-Nearest Neighbor Classifiers","date":"2018-05-06","arxiv_id":"1805.02294","n_code_links":0,"syntology":null},{"paper":null,"slug":"bone-marrow-cells-detection-a-technique-for","title":"Bone marrow cells detection: A technique for the microscopic image analysis","date":"2018-05-05","arxiv_id":"1805.02058","n_code_links":0,"syntology":null},{"paper":"/paper/unsupervised-feature-learning-via-non","slug":"unsupervised-feature-learning-via-non","title":"Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination","date":"2018-05-05","arxiv_id":"1805.01978","n_code_links":15,"syntology":{"ran":19,"of":23,"n_ran_checked":17,"n_instrument":2,"unverified":4,"pointer_only":16,"phrase":"19 ran (of which 14 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","official":{"repos":["zhirongw/lemniscate.pytorch"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"counterfactual-learning-to-rank-for-additive","title":"A General Framework for Counterfactual Learning-to-Rank","date":"2018-04-30","arxiv_id":"1805.00065","n_code_links":0,"syntology":null},{"paper":null,"slug":"big-data-quantum-support-vector-clustering","title":"An Investigation on Support Vector Clustering for Big Data in Quantum Paradigm","date":"2018-04-29","arxiv_id":"1804.10905","n_code_links":0,"syntology":null},{"paper":"/paper/local-learning-with-deep-and-handcrafted","slug":"local-learning-with-deep-and-handcrafted","title":"Local Learning with Deep and Handcrafted Features for Facial Expression Recognition","date":"2018-04-29","arxiv_id":"1804.10892","n_code_links":0,"syntology":null},{"paper":null,"slug":"smart-surveillance-as-an-edge-network-service","title":"Smart Surveillance as an Edge Network Service: from Harr-Cascade, SVM to a Lightweight CNN","date":"2018-04-24","arxiv_id":"1805.00331","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-support-tensor-train-machine","title":"A Support Tensor Train Machine","date":"2018-04-17","arxiv_id":"1804.06114","n_code_links":0,"syntology":null},{"paper":"/paper/sparse-unsupervised-capsules-generalize","slug":"sparse-unsupervised-capsules-generalize","title":"Sparse Unsupervised Capsules Generalize Better","date":"2018-04-17","arxiv_id":"1804.06094","n_code_links":1,"syntology":null},{"paper":null,"slug":"minimal-support-vector-machine","title":"Minimal Support Vector Machine","date":"2018-04-06","arxiv_id":"1804.02370","n_code_links":0,"syntology":null},{"paper":null,"slug":"precision-sugarcane-monitoring-using-svm","title":"Precision Sugarcane Monitoring Using SVM Classifier","date":"2018-03-26","arxiv_id":"1803.09413","n_code_links":0,"syntology":null},{"paper":null,"slug":"stance-detection-on-tweets-an-svm-based","title":"Stance Detection on Tweets: An SVM-based Approach","date":"2018-03-23","arxiv_id":"1803.08910","n_code_links":0,"syntology":null},{"paper":"/paper/early-hospital-mortality-prediction-using","slug":"early-hospital-mortality-prediction-using","title":"Early hospital mortality prediction using vital signals","date":"2018-03-18","arxiv_id":"1803.06589","n_code_links":1,"syntology":null},{"paper":null,"slug":"automated-detection-and-segmentation-of-non","title":"Automated detection and segmentation of non-mass enhancing breast tumors with dynamic contrast-enhanced magnetic resonance imaging","date":"2018-03-12","arxiv_id":"1803.04200","n_code_links":0,"syntology":null},{"paper":null,"slug":"early-start-intention-detection-of-cyclists","title":"Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network","date":"2018-03-06","arxiv_id":"1803.02242","n_code_links":0,"syntology":null},{"paper":null,"slug":"tongue-image-constitution-recognition-based","title":"Tongue image constitution recognition based on Complexity Perception method","date":"2018-03-01","arxiv_id":"1803.00219","n_code_links":0,"syntology":null},{"paper":null,"slug":"spatially-constrained-location-prior-for","title":"Spatially Constrained Location Prior for Scene Parsing","date":"2018-02-24","arxiv_id":"1802.08790","n_code_links":0,"syntology":null},{"paper":null,"slug":"adaptive-specular-reflection-detection-and","title":"Adaptive specular reflection detection and inpainting in colonoscopy video frames","date":"2018-02-23","arxiv_id":"1802.08402","n_code_links":0,"syntology":null},{"paper":"/paper/anomaly-detection-using-one-class-neural","slug":"anomaly-detection-using-one-class-neural","title":"Anomaly Detection using One-Class Neural Networks","date":"2018-02-18","arxiv_id":"1802.06360","n_code_links":4,"syntology":{"ran":2,"of":2,"n_ran_checked":2,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["raghavchalapathy/oc-nn"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"paper":null,"slug":"scalable-alignment-kernels-via-space","title":"Space-efficient Feature Maps for String Alignment Kernels","date":"2018-02-18","arxiv_id":"1802.06382","n_code_links":0,"syntology":null},{"paper":null,"slug":"global-scale-phylogenetic-linguistic","title":"Global-scale phylogenetic linguistic inference from lexical resources","date":"2018-02-17","arxiv_id":"1802.06079","n_code_links":0,"syntology":null},{"paper":null,"slug":"ntmaldetect-a-machine-learning-approach-to","title":"NtMalDetect: A Machine Learning Approach to Malware Detection Using Native API System Calls","date":"2018-02-15","arxiv_id":"1802.05412","n_code_links":0,"syntology":null},{"paper":null,"slug":"500-times-faster-than-deep-learning-a-case","title":"500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)","date":"2018-02-14","arxiv_id":"1802.05319","n_code_links":0,"syntology":null},{"paper":null,"slug":"bridge-type-classification-supervised","title":"Bridge type classification: supervised learning on a modified NBI dataset","date":"2018-02-12","arxiv_id":"1803.04478","n_code_links":0,"syntology":null},{"paper":null,"slug":"histogram-of-oriented-depth-gradients-for","title":"Histogram of Oriented Depth Gradients for Action Recognition","date":"2018-01-29","arxiv_id":"1801.09477","n_code_links":0,"syntology":null},{"paper":null,"slug":"feature-based-framework-to-detect-diseases","title":"Feature Based Framework to Detect Diseases, Tumor, and Bleeding in Wireless Capsule Endoscopy","date":"2018-01-27","arxiv_id":"1802.02232","n_code_links":0,"syntology":null},{"paper":null,"slug":"solving-for-multi-class-using-orthogonal","title":"Solving for multi-class using orthogonal coding matrices","date":"2018-01-27","arxiv_id":"1801.09055","n_code_links":0,"syntology":null},{"paper":"/paper/deeplung-deep-3d-dual-path-nets-for-automated","slug":"deeplung-deep-3d-dual-path-nets-for-automated","title":"DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification","date":"2018-01-25","arxiv_id":"1801.09555","n_code_links":2,"syntology":null},{"paper":"/paper/transfer-learning-for-improving-speech","slug":"transfer-learning-for-improving-speech","title":"Transfer Learning for Improving Speech Emotion Classification Accuracy","date":"2018-01-19","arxiv_id":"1801.06353","n_code_links":1,"syntology":null},{"paper":"/paper/detecting-abnormal-events-in-video-using","slug":"detecting-abnormal-events-in-video-using","title":"Detecting abnormal events in video using Narrowed Normality Clusters","date":"2018-01-12","arxiv_id":"1801.05030","n_code_links":0,"syntology":null},{"paper":"/paper/adversarial-deep-learning-for-robust","slug":"adversarial-deep-learning-for-robust","title":"Adversarial Deep Learning for Robust Detection of Binary Encoded Malware","date":"2018-01-09","arxiv_id":"1801.02950","n_code_links":2,"syntology":{"ran":1,"of":1,"n_ran_checked":1,"n_instrument":0,"unverified":0,"pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","official":{"repos":["ALFA-group/robust-adv-malware-detection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"paper":null,"slug":"fusion-of-ann-and-svm-classifiers-for-network","title":"Fusion of ANN and SVM Classifiers for Network Attack Detection","date":"2018-01-09","arxiv_id":"1801.02746","n_code_links":0,"syntology":null},{"paper":null,"slug":"learning-automata-based-svm-for-intrusion","title":"Learning automata based SVM for intrusion detection","date":"2018-01-04","arxiv_id":"1801.01314","n_code_links":0,"syntology":null},{"paper":null,"slug":"kernel-graph-convolutional-neural-nets","title":"Kernel Graph Convolutional Neural Nets","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"small-coresets-to-represent-large-training","title":"Small Coresets to Represent Large Training Data for Support Vector Machines","date":"2018-01-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"an-empirical-evaluation-for-the-intrusion","title":"An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods","date":"2017-12-27","arxiv_id":"1712.09623","n_code_links":0,"syntology":null},{"paper":null,"slug":"detection-and-classification-of-masses-in","title":"Detection and classification of masses in mammographic images in a multi-kernel approach","date":"2017-12-20","arxiv_id":"1712.07116","n_code_links":0,"syntology":null},{"paper":"/paper/foldingnet-point-cloud-auto-encoder-via-deep","slug":"foldingnet-point-cloud-auto-encoder-via-deep","title":"FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation","date":"2017-12-19","arxiv_id":"1712.07262","n_code_links":3,"syntology":null},{"paper":null,"slug":"combining-deep-universal-features-semantic","title":"Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning","date":"2017-12-08","arxiv_id":"1712.03151","n_code_links":0,"syntology":null},{"paper":null,"slug":"using-svdd-in-simplemkl-for-3d-shapes","title":"Using SVDD in SimpleMKL for 3D-Shapes Filtering","date":"2017-12-07","arxiv_id":"1712.02658","n_code_links":0,"syntology":null},{"paper":null,"slug":"automatic-recognition-of-coal-and-gangue","title":"Automatic Recognition of Coal and Gangue based on Convolution Neural Network","date":"2017-12-03","arxiv_id":"1712.00720","n_code_links":0,"syntology":null},{"paper":null,"slug":"3d-facial-action-units-recognition-for","title":"3D Facial Action Units Recognition for Emotional Expression","date":"2017-12-01","arxiv_id":"1712.00195","n_code_links":0,"syntology":null},{"paper":null,"slug":"a-new-alternating-direction-method-for-linear","title":"A New Alternating Direction Method for Linear Programming","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"decomposition-invariant-conditional-gradient","title":"Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/efficient-approximation-algorithms-for","slug":"efficient-approximation-algorithms-for","title":"Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification","date":"2017-12-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/hunt-for-the-unique-stable-sparse-and-fast","slug":"hunt-for-the-unique-stable-sparse-and-fast","title":"Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs","date":"2017-12-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"iiit-h-at-ijcnlp-2017-task-4-customer","title":"IIIT-H at IJCNLP-2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"max-margin-invariant-features-from-1","title":"Max-Margin Invariant Features from Transformed Unlabelled Data","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"process-constrained-batch-bayesian","title":"Process-constrained batch Bayesian optimisation","date":"2017-12-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":"/paper/protein-interface-prediction-using-graph","slug":"protein-interface-prediction-using-graph","title":"Protein Interface Prediction using Graph Convolutional Networks","date":"2017-12-01","arxiv_id":null,"n_code_links":2,"syntology":null},{"paper":null,"slug":"subject-selection-on-a-riemannian-manifold","title":"Subject Selection on a Riemannian Manifold for Unsupervised Cross-subject Seizure Detection","date":"2017-12-01","arxiv_id":"1712.00465","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-analysis-of-breast-mris-for","title":"Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ","date":"2017-11-28","arxiv_id":"1711.10577","n_code_links":0,"syntology":null},{"paper":null,"slug":"highly-efficient-human-action-recognition","title":"Highly Efficient Human Action Recognition with Quantum Genetic Algorithm Optimized Support Vector Machine","date":"2017-11-27","arxiv_id":"1711.09511","n_code_links":0,"syntology":null},{"paper":null,"slug":"pulsar-candidate-identification-with","title":"Pulsar Candidate Identification with Artificial Intelligence Techniques","date":"2017-11-27","arxiv_id":"1711.10339","n_code_links":0,"syntology":null},{"paper":null,"slug":"deep-learning-analysis-of-the-myocardium-in","title":"Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis","date":"2017-11-24","arxiv_id":"1711.08917","n_code_links":0,"syntology":null},{"paper":"/paper/practical-hash-functions-for-similarity","slug":"practical-hash-functions-for-similarity","title":"Practical Hash Functions for Similarity Estimation and Dimensionality Reduction","date":"2017-11-23","arxiv_id":"1711.08797","n_code_links":1,"syntology":null},{"paper":null,"slug":"training-large-margin-host-pathogen-protein","title":"Training large margin host-pathogen protein-protein interaction predictors","date":"2017-11-21","arxiv_id":"1711.07886","n_code_links":0,"syntology":null},{"paper":"/paper/light-head-r-cnn-in-defense-of-two-stage","slug":"light-head-r-cnn-in-defense-of-two-stage","title":"Light-Head R-CNN: In Defense of Two-Stage Object Detector","date":"2017-11-20","arxiv_id":"1711.07264","n_code_links":5,"syntology":null},{"paper":"/paper/a-fusion-based-gender-recognition-method","slug":"a-fusion-based-gender-recognition-method","title":"A Fusion-based Gender Recognition Method Using Facial Images","date":"2017-11-17","arxiv_id":"1711.06451","n_code_links":1,"syntology":null},{"paper":null,"slug":"visual-concepts-and-compositional-voting","title":"Visual Concepts and Compositional Voting","date":"2017-11-13","arxiv_id":"1711.04451","n_code_links":0,"syntology":null},{"paper":"/paper/a-sequence-based-mesh-classifier-for-the","slug":"a-sequence-based-mesh-classifier-for-the","title":"A Sequence-Based Mesh Classifier for the Prediction of Protein-Protein Interactions","date":"2017-11-12","arxiv_id":"1711.04294","n_code_links":1,"syntology":null},{"paper":null,"slug":"hand-gesture-recognition-with-leap-motion","title":"Hand Gesture Recognition with Leap Motion","date":"2017-11-12","arxiv_id":"1711.04293","n_code_links":0,"syntology":null},{"paper":null,"slug":"dimension-reduction-of-high-dimensional","title":"Dimension Reduction of High-Dimensional Datasets Based on Stepwise SVM","date":"2017-11-09","arxiv_id":"1711.03346","n_code_links":0,"syntology":null},{"paper":null,"slug":"intelligent-fault-analysis-in-electrical","title":"Intelligent Fault Analysis in Electrical Power Grids","date":"2017-11-08","arxiv_id":"1711.03026","n_code_links":0,"syntology":null},{"paper":null,"slug":"authorship-analysis-of-xenophons-cyropaedia","title":"Authorship Analysis of Xenophon's Cyropaedia","date":"2017-11-06","arxiv_id":"1711.01684","n_code_links":0,"syntology":null},{"paper":null,"slug":"gaussian-kernel-in-quantum-paradigm","title":"Gaussian Kernel in Quantum Learning","date":"2017-11-04","arxiv_id":"1711.01464","n_code_links":0,"syntology":null},{"paper":null,"slug":"an-ensemble-method-with-sentiment-features","title":"An Ensemble Method with Sentiment Features and Clustering Support","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null},{"paper":null,"slug":"chemical-induced-disease-detection-using","title":"Chemical-Induced Disease Detection Using Invariance-based Pattern Learning Model","date":"2017-11-01","arxiv_id":null,"n_code_links":0,"syntology":null}],"record_sha256":"326a2433862aea194566e4efa5390a9347624abfcaa7ac131f9a32dd3c3138f7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}