SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3276–3300 of 14581 papers

TitleStatusHype
Angle-Based Cost-Sensitive Multicategory Classification—0
AL2: Progressive Activation Loss for Learning General Representations in Classification Neural NetworksCode0
ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification—0
Brazilian Lyrics-Based Music Genre Classification Using a BLSTM Network—0
SimLoss: Class Similarities in Cross EntropyCode1
CNN-based Repetitive self-revised learning for photos' aesthetics imbalanced classification—0
Application of Support Vector Machines for Seismogram Analysis and Differentiation—0
Sensitive Data Detection and Classification in Spanish Clinical Text: Experiments with BERT—0
Meta-SVDD: Probabilistic Meta-Learning for One-Class Classification in Cancer Histology Images—0
Transfer Learning for Information Extraction with Limited Data—0
TaskNorm: Rethinking Batch Normalization for Meta-LearningCode1
AIDeveloper: deep learning image classification in life science and beyondCode1
On the performance of deep learning models for time series classification in streamingCode1
Optimizing JPEG Quantization for Classification Networks—0
Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification—0
End-to-End Trainable One-Stage Parking Slot Detection Integrating Global and Local Information—0
Minimal spiking neuron for solving multi-label classification tasks—0
PAC-Bayes meta-learning with implicit task-specific posteriors—0
Factorized Graph Representations for Semi-Supervised Learning from Sparse DataCode0
Feature Extraction for Hyperspectral Imagery: The Evolution from Shallow to Deep (Overview and Toolbox)Code1
Towards Fair Cross-Domain Adaptation via Generative Learning—0
Reduced Dilation-Erosion Perceptron for Binary ClassificationCode0
Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural NetworksCode0
On the Learning Property of Logistic and Softmax Losses for Deep Neural NetworksCode0
Contrastive estimation reveals topic posterior information to linear models—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