Compared to other regression techniques, a well-tuned neural network regression system can produce the most accurate prediction model, says Dr. James McCaffrey of Microsoft Research in presenting this ...

A regression problem is one where the goal is to predict a single numeric value. For example, you might want to predict the annual income of a person based on their sex, age, state where they live and ...

Researchers use a machine learning (ML) approach to obtain the EM-aware aging prediction of the power grid (PG) network. They use neural network–based regression as their core ML technique to ...

Bankruptcy prediction has traditionally relied on statistical approaches such as Altman’s Z-score, which use financial ratios ...

Increasingly, the trend in machine learning forms of artificial intelligence is toward larger and larger neural networks. The biggest neural nets, such as such as Google's Pathways Language Model, as ...

Math is the language of the physical world, and some see mathematical patterns everywhere: in weather, in the way soundwaves move, and even in the spots or stripes zebra fish develop in embryos. Math ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy ...

With artificial intelligence and machine learning (AI/ML) processors and coprocessors roaring across the embedded edge product landscape, the quest continues for high-performance technology that can ...

Confused by neural networks? This video breaks it all down in simple terms. Understand how they work and why they’re at the core of modern machine learning. #MachineLearning #NeuralNetworks ...

A resistor that works in a similar way to nerve cells in the body could be used to build neural networks for machine learning. Many large machine learning models rely on increasing amounts of ...