SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3951–3975 of 14581 papers

TitleStatusHype
Cross-media User Profiling with Joint Textual and Social User Embedding—0
Binary classification with ambiguous training data—0
Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification—0
Cross-Modal Message Passing for Two-stream Fusion—0
Cross-Modal Retrieval Augmentation for Multi-Modal Classification—0
Attentive Prototype Few-shot Learning with Capsule Network-based Embedding—0
Cross-Target Stance Classification with Self-Attention Networks—0
Cross-task pre-training for on-device acoustic scene classification—0
Crowd-Powered Data Mining—0
Binary Classification with XOR Queries: Fundamental Limits and An Efficient Algorithm—0
Crowdsourced PAC Learning under Classification Noise—0
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English—0
Binary Classification using Pairs of Minimum Spanning Trees or N-ary Trees—0
CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification—0
CruzAffect at AffCon 2019 Shared Task: A feature-rich approach to characterize happiness—0
Cryptocurrency Price Prediction and Trading Strategies Using Support Vector Machines—0
CrystalNest at SemEval-2017 Task 4: Using Sarcasm Detection for Enhancing Sentiment Classification and Quantification—0
CS591 Report: Application of siamesa network in 2D transformation—0
CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification—0
CSAL: Self-adaptive Labeling based Clustering Integrating Supervised Learning on Unlabeled Data—0
CSIFT Based Locality-constrained Linear Coding for Image Classification—0
CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for Imbalanced Data Classification—0
Revisiting Classification Perspective on Scene Text Recognition—0
Attribute-Modulated Generative Meta Learning for Zero-Shot Classification—0
An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates—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