SOTAVerified

General Classification

Algorithms trying to solve the general task of classification.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 9576–9600 of 14581 papers

TitleStatusHype
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes—0
Using Deep Learning Method for Classification: A Proposed Algorithm for the ISIC 2017 Skin Lesion Classification Challenge—0
Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour—0
Using Dimension Reduction to Improve the Classification of High-dimensional Data—0
Coherent and Controllable Outfit Generation—0
Using Distributional Thesaurus Embedding for Co-hyponymy Detection—0
Using Ensemble Models in the Histological Examination of Tissue Abnormalities—0
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification—0
Using Gaussian Processes for Rumour Stance Classification in Social Media—0
Using Generic Summarization to Improve Music Information Retrieval Tasks—0
Using Graphs of Classifiers to Impose Declarative Constraints on Semi-supervised Learning—0
Using Hankel Matrices for Dynamics-based Facial Emotion Recognition and Pain Detection—0
Using Hedge Detection to Improve Committed Belief Tagging—0
Using KL-divergence to focus Deep Visual Explanation—0
Using Latent Variable Models to Observe Academic Pathways—0
Using learned optimizers to make models robust to input noise—0
Using machine learning and information visualisation for discovering latent topics in Twitter news—0
Using Machine Learning and Natural Language Processing Techniques to Analyze and Support Moderation of Student Book Discussions—0
Using machine learning for medium frequency derivative portfolio trading—0
Using Machine Learning to Automate Mammogram Images Analysis—0
Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification—0
Using objective words in the reviews to improve the colloquial arabic sentiment analysis—0
Using Ontologies To Improve Performance In Massively Multi-label Prediction—0
Using Ontologies To Improve Performance In Massively Multi-label Prediction Models—0
Using Paraphrasing and Memory-Augmented Models to Combat Data Sparsity in Question Interpretation with a Virtual Patient Dialogue System—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