Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

Abstract: This paper proposes and analyzes a gradient-type algorithm based on Burer-Monteiro factorization, called the Asymmetric Projected Gradient Descent (APGD), for reconstructing the point set ...

It's been only a few months since OpenAI released its last big improvement to AI image generations in ChatGPT and through its application programming interface (API) — namely, a new image generation ...

Key Laboratory of Optimization Theory and Applications, School of Mathematical Sciences, China West Normal University, Nanchong, China. Overall, existing literature mainly focuses on smooth ...

Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single numeric value. The demo uses stochastic gradient descent, one of two ...

We manipulate the structure of an artificial neural network to isolate the influence of anatomical architecture on the emergence of the topological gradient of activation pattern propagation observed ...

The rise of AI, graphic processing, combinatorial optimization and other data-intensive applications has resulted in data-processing bottlenecks, as ever greater amounts of data must be shuttled back ...

Abstract: Bi-conjugate Gradient Method (BCG) has potential problems on slow convergence or divergence when complex linear equations are large-scale or coefficient matrix of complex linear equations is ...

A gradient preconditioning approach based on transmitted wave energy for least-squares reverse time migration (LSRTM) is proposed in this study. The gradient is preconditioned by using the energy of ...

As artificial intelligence (AI) applications become ubiquitous in medical care, autonomous driving, robotics, and other fields, accuracy requirements and neural network complexity increase in tandem, ...