Search Results for author:
Found 7 papers, 0 papers with code
Distributed Estimation with Partially Accessible Information: An IMAT Approach to LMS Diffusion
Distributed algorithms, particularly Diffusion Least Mean Square, are widely favored for their reliability, robustness, and fast convergence in various industries.
Augmenting SEFDM Performance in High-Doppler Channels
This paper presents an innovative approach leveraging Spectrally Efficient Frequency Division Multiplexing (SEFDM) with enhancements, including Frequency Domain Cyclic Prefix (FDCP) and Modified Non-Linear (MNL) acceleration, to address challenges arising from delay and Doppler shift in mobile communication channels.
Separability and Scatteredness (S&S) Ratio-Based Efficient SVM Regularization Parameter, Kernel, and Kernel Parameter Selection
By stochastically analyzing the behavior of the regularization parameter, this work shows that the SVM performance can be modeled as a function of separability and scatteredness (S&S) of the data.
Learnability, Sample Complexity, and Hypothesis Class Complexity for Regression Models
The proposed approach, denoted by epsilon-Confidence Approximately Correct (epsilon CoAC), utilizes Kullback Leibler divergence (relative entropy) and proposes a new related typical set in the set of hyperparameters to tackle the learnability issue.
Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning
This paper aims to offer an approach using Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading strategy in the stock and cryptocurrency markets.
Distributed interference cancellation in multi-agent scenarios
This paper considers the problem of detecting impaired and noisy nodes over network.
A Nonlinear Acceleration Method for Iterative Algorithms
Iterative methods have led to better understanding and solving problems such as missing sampling, deconvolution, inverse systems, impulsive and Salt and Pepper noise removal problems.
