SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

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Showing 4451–4475 of 14581 papers

TitleStatusHype
Learning and Interpreting Multi-Multi-Instance Learning Networks—0
Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks—0
Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences—0
Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks—0
Learning an Integrated Distance Metric for Comparing Structure of Complex Networks—0
Learning An Invariant Speech Representation—0
Learning a Representation with the Block-Diagonal Structure for Pattern Classification—0
Learning audio sequence representations for acoustic event classification—0
Learning Augmentation Network via Influence Functions—0
Learning Based Approaches for Vietnamese Question Classification Using Keywords Extraction from the Web—0
Learning-based Compression for Material and Texture Recognition—0
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification—0
Learning Bilingual Word Representations by Marginalizing Alignments—0
Learning Binary Codes and Binary Weights for Efficient Classification—0
Learning Binary Codes for High-Dimensional Data Using Bilinear Projections—0
Learning Boolean functions with concentrated spectra—0
Learning cascaded latent variable models for biomedical text classification—0
Learning Classifiers from Synthetic Data Using a Multichannel Autoencoder—0
Learning Class-to-Image Distance with Object Matchings—0
Learning Clusterable Visual Features for Zero-Shot Recognition—0
Learning CNN filters from user-drawn image markers for coconut-tree image classification—0
Learning Coarse-to-Fine Sparselets for Efficient Object Detection and Scene Classification—0
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network—0
Learning Compact Structural Representations for Audio Events Using Regressor Banks—0
Learning, compression, and leakage: Minimising classification error via meta-universal compression principles—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