We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …

We present a survey of ways in which existing scientific knowledge are included when
constructing models with neural networks. The inclusion of domain-knowledge is of special …

Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …

Combining deep neural networks with structured logic rules is desirable to harness flexibility
and reduce uninterpretability of the neural models. We propose a general framework …

Motivation: Modern data acquisition based on high-throughput technology is often facing the
problem of missing data. Algorithms commonly used in the analysis of such large-scale data …

Intelligent agents are employed as the central characters in this new introductory text.
Beginning with elementary reactive agents, Nilsson gradually increases their cognitive …

In this paper we present an average-case analysis of the Bayesian classi er, a simple
probabilistic induction algorithm that fares remarkably well on many learning tasks. Our …

Credit Scoring and Its Applications, Second Edition : Back Matter Page 1 Bibliography [1]
Acharya, VV, Bharath, ST, and Srinivasan, A. (2007) Does industry-wide distress affect …

Abstract Theory refinement is the task of updating a domain theory in the light of new cases,
to be done automatically or with some expert assistance. The problem of theory refinement …

Hybrid learning methods use theoretical knowledge of a domain and a set of classified
examples to develop a method for accurately classifying examples not seen during training …