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Found 8 papers, 0 papers with code
Hyperspectral and LiDAR data classification based on linear self-attention
An efficient linear self-attention fusion model is proposed in this paper for the task of hyperspectral image (HSI) and LiDAR data joint classification.
Hierarchical Text Interaction for Rating Prediction
At review level, we mutually propagate textual features between the user and item, and capture the informative reviews.
Learning through deterministic assignment of hidden parameters
Supervised learning frequently boils down to determining hidden and bright parameters in a parameterized hypothesis space based on finite input-output samples.
Learning and approximation capability of orthogonal super greedy algorithm
We consider the approximation capability of orthogonal super greedy algorithms (OSGA) and its applications in supervised learning.
Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part II)
On one hand, we find that the randomness causes an additional uncertainty problem of ELM, both in approximation and learning.
Learning rates of $l^q$ coefficient regularization learning with Gaussian kernel
Regularization is a well recognized powerful strategy to improve the performance of a learning machine and $l^q$ regularization schemes with $0<q<\infty$ are central in use.
Compressed Sensing SAR Imaging with Multilook Processing
Multilook processing is a widely used speckle reduction approach in synthetic aperture radar (SAR) imaging.
Does generalization performance of $l^q$ regularization learning depend on $q$? A negative example
To facilitate the use of $l^{q}$-regularization, we intend to seek for a modeling strategy where an elaborative selection on $q$ is avoidable.
