SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

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Showing 3926–3950 of 14581 papers

TitleStatusHype
Cross-Domain Sentiment Classification with Target Domain Specific Information—0
Binary Classification with Classical Instances and Quantum Labels—0
Binary Classification with Bounded Abstention Rate—0
Cross-domain Text Classification with Multiple Domains and Disparate Label Sets—0
Cross-label Suppression: A Discriminative and Fast Dictionary Learning with Group Regularization—0
Ambiguous Proximity Distribution—0
Cross-Lingual Classification of Topics in Political Texts—0
Cross-lingual Dataless Classification for Languages with Small Wikipedia Presence—0
Cross-lingual Data Transformation and Combination for Text Classification—0
A Comparative Study on Linguistic Feature Selection in Sentiment Polarity Classification—0
Decoding Imagined Speech using Wavelet Features and Deep Neural Networks—0
Attention Modeling for Targeted Sentiment—0
Cross-lingual Extended Named Entity Classification of Wikipedia Articles—0
Decoding of visual-related information from the human EEG using an end-to-end deep learning approach—0
Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation—0
Cross-lingual intent classification in a low resource industrial setting—0
Cross-Lingual Mixture Model for Sentiment Classification—0
Binary classification with ambiguous training data—0
Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning—0
Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees—0
Cross-Lingual Task-Specific Representation Learning for Text Classification in Resource Poor Languages—0
Cross-lingual Text Classification Using Topic-Dependent Word Probabilities—0
Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning Based Image Classification—0
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning—0
An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates—0
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All datasetsXORirisActivityFashion-MNISTISOLETMice ProteinMNISTWine

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