General Classification
3929 papers with code • 11 benchmarks • 8 datasets
Algorithms trying to solve the general task of classification.
Benchmarks
These leaderboards are used to track progress in General Classification
Libraries
Use these libraries to find General Classification models and implementations
MNIST
Fashion-MNIST
Adult
iris
Hyper-Kvasir Dataset
Wine
Kvasir-Capsule
CVR
Latest papers with no code
Large Stepsize Gradient Descent for Logistic Loss: Non-Monotonicity of the Loss Improves Optimization Efficiency
We consider gradient descent (GD) with a constant stepsize applied to logistic regression with linearly separable data, where the constant stepsize $\eta$ is so large that the loss initially oscillates.
Improving performance of heart rate time series classification by grouping subjects
Together, these findings indicate that heart rate time series can be utilized for classification tasks like predicting activity.
Explainable and Accurate Natural Language Understanding for Voice Assistants and Beyond
Therefore to bridge this gap, we transform the full joint NLU model to be `inherently' explainable at granular levels without compromising on accuracy.
CEIL: A General Classification-Enhanced Iterative Learning Framework for Text Clustering
To address this issue, we propose CEIL, a novel Classification-Enhanced Iterative Learning framework for short text clustering, which aims at generally promoting the clustering performance by introducing a classification objective to iteratively improve feature representations.
Video traffic identification with novel feature extraction and selection method
Second, to reduce the cost of video traffic identification and select an effective feature subset, the current research proposes an adaptive distribution distance-based feature selection (ADDFS) method, which uses Wasserstein distance to measure the distance between feature distributions.
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal
This paper discusses and formalizes the synergy between Bayesian optimization and active learning as symbiotic adaptive sampling methodologies driven by common principles.
Sparsity based morphological identification of heartbeats
The proposal considers the sparsity of the representation of a heartbeat as a parameter for morphological identification.
Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing
First, we adopt the particle swarm optimization (PSO) algorithm to speed up the search of a discriminant dimension that is expressed as a linear combination of current dimensions.
Exploring Techniques for the Analysis of Spontaneous Asynchronicity in MPI-Parallel Applications
This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs.
Minimax risk classifiers with 0-1 loss
Supervised classification techniques use training samples to learn a classification rule with small expected 0-1 loss (error probability).
