Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two
hitherto distinct approaches.” Neuro” refers to the artificial neural networks prominent in …

The notions of distance and similarity play a key role in many machine learning approaches,
and artificial intelligence in general, since they can serve as an organizing principle by …

Перед вами один из самых интересных учебников по машинному обучению–разделу
искусственного интеллекта, изучающего методы построения моделей, способных …

The study and understanding of human behaviour is relevant to computer science, artificial
intelligence, neural computation, cognitive science, philosophy, psychology, and several …

As one of the most comprehensive machine learning texts around, this book does justice to
the field's incredible richness, but without losing sight of the unifying principles. Peter Flach's …

Many underlying relationships among data in several areas of science and engineering, eg,
computer vision, molecular chemistry, molecular biology, pattern recognition, and data …

Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …

Rules–the clearest, most explored and best understood form of knowledge representation–
are particularly important for data mining, as they offer the best tradeoff between human and …

We propose a method to combine the interpretability and expressive power of first-order
logic with the effectiveness of neural network learning. In particular, we introduce a lifted …

Relational learning can be described as the task of learning first-order logic rules from
examples. It has enabled a number of new machine learning applications, eg graph mining …