{"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":"/task/classification/papers/128","list_of":"/task/classification","task":"General Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":128,"pages_in_order":146,"rows_per_page":100,"rows":[12701,12800],"of":14581,"counts":{"archive_papers_tagged":14581,"with_a_code_link":3945,"where_syntology_ran_a_sample":713,"not_listed_spam_title":0,"listed":14581,"listed_where_code_ran":713,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":560,"every_run_a_failure_of_syntologys_instrument":153,"listed_with_a_run_with_no_instrument_failure":560,"listed_every_run_a_failure_of_syntologys_instrument":153,"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":"/task/classification","prev":"/task/classification/papers/127","next":"/task/classification/papers/129","papers":[{"url":null,"slug":"self-organizing-maps-for-classification-of-a","title":"Self-Organizing Maps for Classification of a Multi-Labeled Corpus","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"semi-supervised-zero-shot-classification-with","title":"Semi-Supervised Zero-Shot Classification With Label Representation Learning","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"simultaneous-foreground-detection-and","title":"Simultaneous Foreground Detection and Classification With Hybrid Features","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-local-embeddings-for-extreme-multi","title":"Sparse Local Embeddings for Extreme Multi-label Classification","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"spherical-random-features-for-polynomial","title":"Spherical Random Features for Polynomial Kernels","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-feature-selection","title":"Structured Feature Selection","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"task-driven-feature-pooling-for-image","title":"Task-Driven Feature Pooling for Image Classification","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"taxonomy-grounded-aggregation-of-classifiers","title":"Taxonomy grounded aggregation of classifiers with different label sets","date":"2015-12-01","arxiv_id":"1512.00355","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-domain-adaptation-with-3","title":"Unsupervised Domain Adaptation With Imbalanced Cross-Domain Data","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"using-skipgrams-bigrams-and-part-of-speech","title":"Using Skipgrams, Bigrams, and Part of Speech Features for Sentiment Classification of Twitter Messages","date":"2015-12-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"aspect-based-opinion-summarization-with","title":"Aspect-based Opinion Summarization with Convolutional Neural Networks","date":"2015-11-30","arxiv_id":"1511.09128","repositories_listed":0,"syntology":null},{"url":null,"slug":"cost-aware-pre-training-for-multiclass-cost","title":"Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning","date":"2015-11-30","arxiv_id":"1511.09337","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-classification-via-mixture-of","title":"Fine-Grained Classification via Mixture of Deep Convolutional Neural Networks","date":"2015-11-30","arxiv_id":"1511.09209","repositories_listed":0,"syntology":null},{"url":null,"slug":"k-nearest-neighbour-classification-of","title":"k-Nearest Neighbour Classification of Datasets with a Family of Distances","date":"2015-11-29","arxiv_id":"1512.00001","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-sentiment-prediction-based","title":"Machine Learning Sentiment Prediction based on Hybrid Document Representation","date":"2015-11-29","arxiv_id":"1511.09107","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-coral-classification-using-deep","title":"Sparse Coral Classification Using Deep Convolutional Neural Networks","date":"2015-11-29","arxiv_id":"1511.09067","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparseness-helps-sparsity-augmented","title":"Sparseness helps: Sparsity Augmented Collaborative Representation for Classification","date":"2015-11-29","arxiv_id":"1511.08956","repositories_listed":0,"syntology":null},{"url":null,"slug":"category-enhanced-word-embedding","title":"Category Enhanced Word Embedding","date":"2015-11-27","arxiv_id":"1511.08629","repositories_listed":0,"syntology":null},{"url":null,"slug":"hierarchical-classification-of-e-commerce","title":"Hierarchical classification of e-commerce related social media","date":"2015-11-26","arxiv_id":"1511.08299","repositories_listed":0,"syntology":null},{"url":null,"slug":"random-forests-for-big-data","title":"Random Forests for Big Data","date":"2015-11-26","arxiv_id":"1511.08327","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-correlation-between-labels-to","title":"Exploring Correlation between Labels to improve Multi-Label Classification","date":"2015-11-25","arxiv_id":"1511.07953","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-universal-paraphrastic-sentence","title":"Towards Universal Paraphrastic Sentence Embeddings","date":"2015-11-25","arxiv_id":"1511.08198","repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-deep-feature-extraction-for","title":"Unsupervised Deep Feature Extraction for Remote Sensing Image Classification","date":"2015-11-25","arxiv_id":"1511.08131","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-identification-of-fixations-saccades","title":"Bayesian Identification of Fixations, Saccades, and Smooth Pursuits","date":"2015-11-24","arxiv_id":"1511.07732","repositories_listed":0,"syntology":null},{"url":null,"slug":"shape-and-symmetry-induction-for-3d-objects","title":"Shape and Symmetry Induction for 3D Objects","date":"2015-11-24","arxiv_id":"1511.07845","repositories_listed":0,"syntology":null},{"url":null,"slug":"interpretable-two-level-boolean-rule-learning","title":"Interpretable Two-level Boolean Rule Learning for Classification","date":"2015-11-23","arxiv_id":"1511.07361","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-linear-models-applied-to-power-quality","title":"Sparse Linear Models applied to Power Quality Disturbance Classification","date":"2015-11-23","arxiv_id":"1511.07281","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-road-surface-wetness-from-audio-a","title":"Detecting Road Surface Wetness from Audio: A Deep Learning Approach","date":"2015-11-22","arxiv_id":"1511.07035","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-pose-prediction-normalization","title":"Fine-grained pose prediction, normalization, and recognition","date":"2015-11-22","arxiv_id":"1511.07063","repositories_listed":0,"syntology":null},{"url":null,"slug":"non-sentential-utterances-in-dialogue","title":"Non-Sentential Utterances in Dialogue: Experiments in Classification and Interpretation","date":"2015-11-22","arxiv_id":"1511.06995","repositories_listed":0,"syntology":null},{"url":null,"slug":"online-sequence-training-of-recurrent-neural","title":"Online Sequence Training of Recurrent Neural Networks with Connectionist Temporal Classification","date":"2015-11-21","arxiv_id":"1511.06841","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-segmentation-of-colon-glands-with","title":"Semantic Segmentation of Colon Glands with Deep Convolutional Neural Networks and Total Variation Segmentation","date":"2015-11-21","arxiv_id":"1511.06919","repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-inference-via-rejection-filtering","title":"Bayesian inference via rejection filtering","date":"2015-11-20","arxiv_id":"1511.06458","repositories_listed":0,"syntology":null},{"url":null,"slug":"l1-logistic-regression-as-a-feature-selection","title":"L1 logistic regression as a feature selection step for training stable classification trees for the prediction of severity criteria in imported malaria","date":"2015-11-20","arxiv_id":"1511.06663","repositories_listed":0,"syntology":null},{"url":null,"slug":"recurrent-semi-supervised-classification-and","title":"Recurrent Semi-supervised Classification and Constrained Adversarial Generation with Motion Capture Data","date":"2015-11-20","arxiv_id":"1511.06653","repositories_listed":0,"syntology":null},{"url":null,"slug":"semantic-diversity-versus-visual-diversity-in","title":"Semantic Diversity versus Visual Diversity in Visual Dictionaries","date":"2015-11-20","arxiv_id":"1511.06704","repositories_listed":0,"syntology":null},{"url":null,"slug":"tempo-feature-endowed-teichmuller-extremal","title":"TEMPO: Feature-Endowed Teichmüller Extremal Mappings of Point Clouds","date":"2015-11-20","arxiv_id":"1511.06624","repositories_listed":0,"syntology":null},{"url":null,"slug":"training-cnns-with-low-rank-filters-for","title":"Training CNNs with Low-Rank Filters for Efficient Image Classification","date":"2015-11-20","arxiv_id":"1511.06744","repositories_listed":0,"syntology":null},{"url":null,"slug":"fast-metric-learning-for-deep-neural-networks","title":"Fast Metric Learning For Deep Neural Networks","date":"2015-11-19","arxiv_id":"1511.06442","repositories_listed":0,"syntology":null},{"url":null,"slug":"faster-method-for-deep-belief-network-based","title":"Faster method for Deep Belief Network based Object classification using DWT","date":"2015-11-19","arxiv_id":"1511.06276","repositories_listed":0,"syntology":null},{"url":null,"slug":"how-much-data-is-needed-to-train-a-medical","title":"How much data is needed to train a medical image deep learning system to achieve necessary high accuracy?","date":"2015-11-19","arxiv_id":"1511.06348","repositories_listed":0,"syntology":null},{"url":null,"slug":"manifold-regularized-deep-neural-networks","title":"Manifold Regularized Deep Neural Networks using Adversarial Examples","date":"2015-11-19","arxiv_id":"1511.06381","repositories_listed":0,"syntology":null},{"url":null,"slug":"multimodal-sparse-representation-learning-and","title":"Multimodal sparse representation learning and applications","date":"2015-11-19","arxiv_id":"1511.06238","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-classification-by-pre-conditioned","title":"Robust Classification by Pre-conditioned LASSO and Transductive Diffusion Component Analysis","date":"2015-11-19","arxiv_id":"1511.06340","repositories_listed":0,"syntology":null},{"url":null,"slug":"structured-prediction-energy-networks","title":"Structured Prediction Energy Networks","date":"2015-11-19","arxiv_id":"1511.06350","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-random-forest-guided-tour","title":"A Random Forest Guided Tour","date":"2015-11-18","arxiv_id":"1511.05741","repositories_listed":0,"syntology":null},{"url":null,"slug":"harvesting-comparable-corpora-and-mining-them","title":"Harvesting comparable corpora and mining them for equivalent bilingual sentences using statistical classification and analogy- based heuristics","date":"2015-11-18","arxiv_id":"1511.06285","repositories_listed":0,"syntology":null},{"url":null,"slug":"studying-the-control-of-non-invasive","title":"Studying the control of non invasive prosthetic hands over large time spans","date":"2015-11-18","arxiv_id":"1511.06004","repositories_listed":0,"syntology":null},{"url":null,"slug":"classifying-and-segmenting-microscopy-images","title":"Classifying and Segmenting Microscopy Images Using Convolutional Multiple Instance Learning","date":"2015-11-17","arxiv_id":"1511.05286","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-structured-inference-neural-networks","title":"Learning Structured Inference Neural Networks with Label Relations","date":"2015-11-17","arxiv_id":"1511.05616","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-classifier-calibration-using-an","title":"Binary Classifier Calibration using an Ensemble of Near Isotonic Regression Models","date":"2015-11-16","arxiv_id":"1511.05191","repositories_listed":0,"syntology":null},{"url":null,"slug":"budget-online-multiple-kernel-learning","title":"Budget Online Multiple Kernel Learning","date":"2015-11-16","arxiv_id":"1511.04813","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-system-for-extracting-sentiment-from-large","title":"A System for Extracting Sentiment from Large-Scale Arabic Social Data","date":"2015-11-15","arxiv_id":"1511.04661","repositories_listed":0,"syntology":null},{"url":null,"slug":"jointly-learning-non-negative-projection-and","title":"Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification","date":"2015-11-14","arxiv_id":"1511.04601","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-fine-grained-features-via-a-cnn-tree","title":"Learning Fine-grained Features via a CNN Tree for Large-scale Classification","date":"2015-11-14","arxiv_id":"1511.04534","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-dense-convolutional-embeddings-for","title":"Learning Dense Convolutional Embeddings for Semantic Segmentation","date":"2015-11-13","arxiv_id":"1511.04377","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-to-sequence-learning-for-optical","title":"Sequence to Sequence Learning for Optical Character Recognition","date":"2015-11-13","arxiv_id":"1511.04176","repositories_listed":0,"syntology":null},{"url":"/paper/pronet-learning-to-propose-object-specific","slug":"pronet-learning-to-propose-object-specific","title":"ProNet: Learning to Propose Object-specific Boxes for Cascaded Neural Networks","date":"2015-11-12","arxiv_id":"1511.03776","repositories_listed":0,"syntology":null},{"url":null,"slug":"datagrinder-fast-accurate-fully-non","title":"DataGrinder: Fast, Accurate, Fully non-Parametric Classification Approach Using 2D Convex Hulls","date":"2015-11-11","arxiv_id":"1511.03576","repositories_listed":0,"syntology":null},{"url":null,"slug":"facial-expression-detection-using-patch-based","title":"Facial Expression Detection using Patch-based Eigen-face Isomap Networks","date":"2015-11-11","arxiv_id":"1511.03363","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-hierarchical-spectral-method-for-extreme","title":"A Hierarchical Spectral Method for Extreme Classification","date":"2015-11-10","arxiv_id":"1511.03260","repositories_listed":0,"syntology":null},{"url":null,"slug":"stochastic-expectation-propagation-for-large","title":"Stochastic Expectation Propagation for Large Scale Gaussian Process Classification","date":"2015-11-10","arxiv_id":"1511.03249","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-radon-cumulative-distribution-transform","title":"The Radon cumulative distribution transform and its application to image classification","date":"2015-11-10","arxiv_id":"1511.03206","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-events-and-key-actors-in-multi","title":"Detecting events and key actors in multi-person videos","date":"2015-11-09","arxiv_id":"1511.02917","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-winner-take-all-approach-to-emotional","title":"A Winner-Take-All Approach to Emotional Neural Networks with Universal Approximation Property","date":"2015-11-08","arxiv_id":"1511.02426","repositories_listed":0,"syntology":null},{"url":null,"slug":"bearing-fault-diagnosis-based-on-spectrum","title":"Bearing fault diagnosis based on spectrum images of vibration signals","date":"2015-11-08","arxiv_id":"1511.02503","repositories_listed":0,"syntology":null},{"url":null,"slug":"performance-analysis-of-multiclass-support","title":"Performance Analysis of Multiclass Support Vector Machine Classification for Diagnosis of Coronary Heart Diseases","date":"2015-11-07","arxiv_id":"1511.02352","repositories_listed":0,"syntology":null},{"url":null,"slug":"review-level-sentiment-classification-with","title":"Review-Level Sentiment Classification with Sentence-Level Polarity Correction","date":"2015-11-07","arxiv_id":"1511.02385","repositories_listed":0,"syntology":null},{"url":null,"slug":"neutralized-empirical-risk-minimization-with","title":"Neutralized Empirical Risk Minimization with Generalization Neutrality Bound","date":"2015-11-06","arxiv_id":"1511.01987","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-empirical-study-on-sentiment","title":"An Empirical Study on Sentiment Classification of Chinese Review using Word Embedding","date":"2015-11-05","arxiv_id":"1511.01665","repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-renyi-classifiers","title":"Discrete Rényi Classifiers","date":"2015-11-05","arxiv_id":"1511.01764","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-classification-based-on-support-vector","title":"Image classification based on support vector machine and the fusion of complementary features","date":"2015-11-05","arxiv_id":"1511.01706","repositories_listed":0,"syntology":null},{"url":null,"slug":"data-stream-classification-using-random","title":"Data Stream Classification using Random Feature Functions and Novel Method Combinations","date":"2015-11-03","arxiv_id":"1511.00971","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-interrogative-utterances-with","title":"Detecting Interrogative Utterances with Recurrent Neural Networks","date":"2015-11-03","arxiv_id":"1511.01042","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-deep-learning-for-question","title":"Distributed Deep Learning for Question Answering","date":"2015-11-03","arxiv_id":"1511.01158","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-an-efficient-multi-class","title":"Toward an Efficient Multi-class Classification in an Open Universe","date":"2015-11-02","arxiv_id":"1511.00725","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-based-diversity-optimization-for","title":"Feature-Based Diversity Optimization for Problem Instance Classification","date":"2015-10-29","arxiv_id":"1510.08568","repositories_listed":0,"syntology":null},{"url":null,"slug":"nonconvex-penalization-in-sparse-estimation","title":"Nonconvex Penalization in Sparse Estimation: An Approach Based on the Bernstein Function","date":"2015-10-29","arxiv_id":"1510.08633","repositories_listed":0,"syntology":null},{"url":null,"slug":"operator-valued-kernels-for-learning-from","title":"Operator-valued Kernels for Learning from Functional Response Data","date":"2015-10-28","arxiv_id":"1510.08231","repositories_listed":0,"syntology":null},{"url":null,"slug":"blitzkriging-kronecker-structured-stochastic","title":"Blitzkriging: Kronecker-structured Stochastic Gaussian Processes","date":"2015-10-27","arxiv_id":"1510.07965","repositories_listed":0,"syntology":null},{"url":null,"slug":"phenotyping-of-clinical-time-series-with-lstm","title":"Phenotyping of Clinical Time Series with LSTM Recurrent Neural Networks","date":"2015-10-26","arxiv_id":"1510.07641","repositories_listed":0,"syntology":null},{"url":null,"slug":"seam-puckering-objective-evaluation-method","title":"Seam Puckering Objective Evaluation Method for Sewing Process","date":"2015-10-25","arxiv_id":"1510.07234","repositories_listed":0,"syntology":null},{"url":null,"slug":"image-parsing-with-a-wide-range-of-classes","title":"Image Parsing with a Wide Range of Classes and Scene-Level Context","date":"2015-10-24","arxiv_id":"1510.07136","repositories_listed":0,"syntology":null},{"url":null,"slug":"generalized-shortest-path-kernel-on-graphs","title":"Generalized Shortest Path Kernel on Graphs","date":"2015-10-22","arxiv_id":"1510.06492","repositories_listed":0,"syntology":null},{"url":null,"slug":"content-adaptive-screen-image-scaling","title":"Content adaptive screen image scaling","date":"2015-10-21","arxiv_id":"1510.06093","repositories_listed":0,"syntology":null},{"url":null,"slug":"whats-the-point-frame-wise-pointing-gesture","title":"What's the point? Frame-wise Pointing Gesture Recognition with Latent-Dynamic Conditional Random Fields","date":"2015-10-20","arxiv_id":"1510.05879","repositories_listed":0,"syntology":null},{"url":null,"slug":"accelerometer-based-activity-classification","title":"Accelerometer based Activity Classification with Variational Inference on Sticky HDP-SLDS","date":"2015-10-19","arxiv_id":"1510.05477","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-semi-supervised-classification-for","title":"Robust Semi-Supervised Classification for Multi-Relational Graphs","date":"2015-10-19","arxiv_id":"1510.06024","repositories_listed":0,"syntology":null},{"url":null,"slug":"normalization-of-relative-and-incomplete","title":"Normalization of Relative and Incomplete Temporal Expressions in Clinical Narratives","date":"2015-10-16","arxiv_id":"1510.04972","repositories_listed":0,"syntology":null},{"url":"/paper/scatter-component-analysis-a-unified","slug":"scatter-component-analysis-a-unified","title":"Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization","date":"2015-10-15","arxiv_id":"1510.04373","repositories_listed":0,"syntology":null},{"url":null,"slug":"inheritance-in-object-oriented-knowledge","title":"Inheritance in Object-Oriented Knowledge Representation","date":"2015-10-14","arxiv_id":"1510.04212","repositories_listed":0,"syntology":null},{"url":null,"slug":"mathematical-foundations-for-designing-and","title":"Mathematical Foundations for Designing and Development of Intelligent Systems of Information Analysis","date":"2015-10-14","arxiv_id":"1510.04183","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-intrinsic-value-of-hfo-features-as-a","title":"The intrinsic value of HFO features as a biomarker of epileptic activity","date":"2015-10-13","arxiv_id":"1510.03507","repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-multiclass-segmentation-using","title":"Interactive multiclass segmentation using superpixel classification","date":"2015-10-12","arxiv_id":"1510.03199","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-of-joint-multi-instance-multi","title":"Evaluation of Joint Multi-Instance Multi-Label Learning For Breast Cancer Diagnosis","date":"2015-10-10","arxiv_id":"1510.02942","repositories_listed":0,"syntology":null},{"url":null,"slug":"omnigraph-rich-representation-and-graph","title":"OmniGraph: Rich Representation and Graph Kernel Learning","date":"2015-10-10","arxiv_id":"1510.02983","repositories_listed":0,"syntology":null},{"url":"/paper/automatic-semantic-classification-of","slug":"automatic-semantic-classification-of","title":"Automatic semantic classification of scientific literature according to the hallmarks of cancer","date":"2015-10-09","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"functional-frank-wolfe-boosting-for-general","title":"Functional Frank-Wolfe Boosting for General Loss Functions","date":"2015-10-09","arxiv_id":"1510.02558","repositories_listed":0,"syntology":null},{"url":null,"slug":"differential-evolution-with-generalized","title":"Differential Evolution with Generalized Mutation Operator for Parameters Optimization in Gene Selection for Cancer Classification","date":"2015-10-08","arxiv_id":"1510.02516","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-knowledge-gradient-with-logistic-belief","title":"The Knowledge Gradient with Logistic Belief Models for Binary Classification","date":"2015-10-08","arxiv_id":"1510.02354","repositories_listed":0,"syntology":null}],"record_sha256":"a09269671c70a4dcc800cf72d1fca255e9659dbf3b3731729d35584e312a6739","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}