SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

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Showing 8501–8550 of 14581 papers

TitleStatusHype
Combining Crowd and Machines for Multi-predicate Item Screening—0
Combining Deep Learning and Linear Processing for Modulation Classification and Symbol Decoding—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Combining Deep Universal Features, Semantic Attributes, and Hierarchical Classification for Zero-Shot Learning—0
Combining dependency information and generalization in a pattern-based approach to the classification of lexical-semantic relation instances—0
Combining Different Features of Idiomaticity for the Automatic Classification of Noun+Verb Expressions in Basque—0
Combining Discourse Markers and Cross-lingual Embeddings for Synonym--Antonym Classification—0
Combining Domain-Specific Meta-Learners in the Parameter Space for Cross-Domain Few-Shot Classification—0
Combining Generative and Discriminative Neural Networks for Sleep Stages Classification—0
Combining High-Level Features of Raw Audio Waves and Mel-Spectrograms for Audio Tagging—0
Combining Lightly-Supervised Text Classification Models for Accurate Contextual Advertising—0
Combining Machine Learning and Social Network Analysis to Reveal the Organizational Structures—0
Combining Multiple Algorithms in Classifier Ensembles using Generalized Mixture Functions—0
Combining Multiple Cues for Visual Madlibs Question Answering—0
Combining multiple resolutions into hierarchical representations for kernel-based image classification—0
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach—0
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection—0
Combining One-Class Classifiers via Meta-Learning—0
Combining Real-Valued and Binary Gabor-Radon Features for Classification and Search in Medical Imaging Archives—0
Combining Recurrent and Convolutional Neural Networks for Relation Classification—0
Combining resources for MWE-token classification—0
Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification—0
Combining Sentinel-1 and Sentinel-2 Time Series via RNN for object-based land cover classification—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification—0
Combining Support Vector Machine and Elephant Herding Optimization for Cardiac Arrhythmias—0
Combining Texture and Shape Cues for Object Recognition With Minimal Supervision—0
Combining Unsupervised Pre-training and Annotator Rationales to Improve Low-shot Text Classification—0
Combining Weakly and Webly Supervised Learning for Classifying Food Images—0
Combining Word-Level and Character-Level Representations for Relation Classification of Informal Text—0
Combining Word Patterns and Discourse Markers for Paradigmatic Relation Classification—0
CommAI: Evaluating the first steps towards a useful general AI—0
Comment: Entropy Learning for Dynamic Treatment Regimes—0
Comment on "Ensemble Projection for Semi-supervised Image Classification"—0
Comments on "Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?"—0
Committees of deep feedforward networks trained with few data—0
Exploring the Interchangeability of CNN Embedding Spaces—0
Common Mode Patterns for Supervised Tensor Subspace Learning—0
Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface—0
Communication-Aware Collaborative Learning—0
Community Aware Random Walk for Network Embedding—0
Community Detection and Classification in Hierarchical Stochastic Blockmodels—0
Como funciona o Deep Learning—0
Compact Deep Neural Networks for Computationally Efficient Gesture Classification From Electromyography Signals—0
Compact Encoding of Words for Efficient Character-level Convolutional Neural Networks Text Classification—0
Compact Feature Learning for Multi-Domain Image Classification—0
Compact Learning for Multi-Label Classification—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Compact retail shelf segmentation for mobile deployment—0
Company classification using machine learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1KNNAccuracy93.1—Unverified
2RFAccuracy92.96—Unverified
3CARTAccuracy92.82—Unverified
4LDAAccuracy77.22—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified
2XBNETAccuracy100—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy42—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy67.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy68.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy13.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy90.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified