Inducing Relational Concepts with Neural Networks via the LINUS System
نویسندگان
چکیده
This paper presents a method to induce relational concepts with neural networks using the inductive logic programming system LINUS. Some first-order inductive learning tasks taken from machine learning literature were applied successfully, thus confirming the quality of the hypothesis generated by neural networks.
منابع مشابه
Modelling of Conventional and Severe Shot Peening Influence on Properties of High Carbon Steel via Artificial Neural Network
Shot peening (SP), as one of the severe plastic deformation (SPD) methods is employed for surface modification of the engineering components by improving the metallurgical and mechanical properties. Furthermore artificial neural network (ANN) has been widely used in different science and engineering problems for predicting and optimizing in the last decade. In the present study, effects of conv...
متن کاملLearning Aggregations and Selections with Relational Neural Networks
We make an assessment of the expressiveness of relational neural networks to learn different kinds of concepts over relational domains. More specifically, we are interested in the distinction between learning aggregation and selection. This distinction plays an important role with regard to current relational learners as most of them have difficulties to make non-trivial combinations of both. A...
متن کاملLifted Relational Neural Networks
We propose a method combining relational-logic representations with deep neural network learning. Domain-specific knowledge is described through relational rules which may be handcrafted or learned. The relational rule-set serves as a template for unfolding possibly deep neural networks whose structures also reflect the structure of given training or testing examples. Different networks corresp...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملReinforcement Learning in Neural Networks: A Survey
In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998