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General Classification

Algorithms trying to solve the general task of classification.

Papers

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Showing 8676–8700 of 14581 papers

TitleStatusHype
Missing Data Prediction and Classification: The Use of Auto-Associative Neural Networks and Optimization Algorithms—0
Missing Features Reconstruction and Its Impact on Classification Accuracy—0
Missing Value Imputation With Unsupervised Backpropagation—0
Mitigating Asymmetric Nonlinear Weight Update Effects in Hardware Neural Network based on Analog Resistive Synapse—0
Mitigating backdoor attacks in LSTM-based Text Classification Systems by Backdoor Keyword Identification—0
Mitigating Class Boundary Label Uncertainty to Reduce Both Model Bias and Variance—0
Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification—0
Mitigating Overfitting in Supervised Classification from Two Unlabeled Datasets: A Consistent Risk Correction Approach—0
MI\&T Lab at SemEval-2017 task 4: An Integrated Training Method of Word Vector for Sentiment Classification—0
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolution Neural Network—0
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations—0
Mixed context networks for semantic segmentation—0
Mixed Formal Learning: A Path to Transparent Machine Learning—0
Mixed Integer Linear Programming for Feature Selection in Support Vector Machine—0
Mixed Neural Network Approach for Temporal Sleep Stage Classification—0
Mixed-norm Regularization for Brain Decoding—0
Mixed-Precision Quantized Neural Network with Progressively Decreasing Bitwidth For Image Classification and Object Detection—0
Mixed State Entanglement Classification using Artificial Neural Networks—0
MIX'EM: Unsupervised Image Classification using a Mixture of Embeddings—0
Mixture model-based functional discriminant analysis for curve classification—0
Mixture of Expert/Imitator Networks: Scalable Semi-supervised Learning Framework—0
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms—0
Mixture separability loss in a deep convolutional network for image classification—0
Mixup-Based Acoustic Scene Classification Using Multi-Channel Convolutional Neural Network—0
Mixup-CAM: Weakly-supervised Semantic Segmentation via Uncertainty Regularization—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