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General Classification

Algorithms trying to solve the general task of classification.

Papers

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Showing 8751–8775 of 14581 papers

TitleStatusHype
Modeling User Leniency and Product Popularity for Sentiment Classification—0
Modelling hidden structure of signals in group data analysis with modified (Lr, 1) and block-term decompositions—0
Synthetic Image Augmentation for Improved Classification using Generative Adversarial Networks—0
Synthetic Sampling for Multi-Class Malignancy Prediction—0
Systematic Comparison of the Influence of Different Data Preprocessing Methods on the Performance of Gait Classifications Using Machine Learning—0
Systematic Testing of Convolutional Neural Networks for Autonomous Driving—0
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications—0
Systèmes du LIA à DEFT'13—0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction—0
TabVec: Table Vectors for Classification of Web Tables—0
Tackling Multilabel Imbalance through Label Decoupling and Data Resampling Hybridization—0
TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition—0
Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification—0
Tag Recommendation by Word-Level Tag Sequence Modeling—0
TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble—0
TakeLab at SemEval-2018 Task 7: Combining Sparse and Dense Features for Relation Classification in Scientific Texts—0
TakeLab-QA at SemEval-2017 Task 3: Classification Experiments for Answer Retrieval in Community QA—0
Taking Advantage of Multitask Learning for Fair Classification—0
Taking the best from the Crowd:Learning Question Passage Classification from Noisy Data—0
Talla at SemEval-2017 Task 3: Identifying Similar Questions Through Paraphrase Detection—0
Talla at SemEval-2018 Task 7: Hybrid Loss Optimization for Relation Classification using Convolutional Neural Networks—0
Tamil Vowel Recognition With Augmented MNIST-like Data Set—0
Robust Adversarial Classification via Abstaining—0
Taming the Cross Entropy Loss—0
Tandem Blocks in Deep Convolutional Neural Networks—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
1XBNETAccuracy100—Unverified
2MONT3Accuracy100—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy42—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy67.7—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy68.5—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy13.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CAEAccuracy90.6—Unverified
#ModelMetricClaimedVerifiedStatus
1MONT3Accuracy100—Unverified