Titlebook: Learning to Learn; Sebastian Thrun,Lorien Pratt Book 1998 Springer Science+Business Media New York 1998 algorithms.artificial neural netwo

Decongestant

The Canonical Distortion Measure for Vector Quantization and Function Approximationd. Common metrics such as the . and . metrics, while mathematically simple, are inappropriate for comparing natural signals such as speech or images. In this paper it is shown how an . of functions on an input space . induces a . (CDM) on X. The depiction “canonical” is justified because it is shown

向下五度才偏

Lifelong Learning Algorithmsoften generalize correctly from only a single training example, even if the number of potentially relevant features is large. To do so, they successfully exploit knowledge acquired in previous learning tasks, to bias subsequent learning..This paper investigates learning in a lifelong context. In con

神化怪物

過于平凡

Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge” Such methods have repeatedly been found to outperform conventional, single-task learning algorithms when the learning tasks are appropriately related. To increase robustness of such approaches, methods are desirable that can reason about the relatedness of individual learning tasks, in order to av

Notorious

Child: A First Step Towards Continual Learningcontinual-learning agent should therefore learn incrementally and hierarchically. This paper describes CHILD, an agent capable of . and .. CHILD can quickly solve complicated non-Markovian reinforcement-learning tasks and can then transfer its skills to similar but even more complicated tasks, learn

onlooker

Reinforcement Learning with Self-Modifying Policiesmodifiable components represented as part of the policy, then we speak of a self-modifying policy (SMP). SMPs can modify the way they modify themselves etc. They are of interest in situations where the initial learning algorithm itself can be improved by experience — this is what we call “l(fā)earning t

離開真充足

Creating Advice-Taking Reinforcement Learnersof training episodes. We present and evaluate a design that addresses this shortcoming by allowing a connectionist Q-learner to accept advice given, at any time and in a natural manner, by an external observer. In our approach, the advice-giver watches the learner and occasionally makes suggestions,

字形刻痕

清醒

Learning to Learn: Introduction and Overviewns. Generic techniques such as decision trees and artificial neural networks, for example, are now being used in various commercial and industrial applications (see e.g., [Langley, 1992; Widrow et al., 1994]).

連接

Theoretical Models of Learning to Learnom the environment [Baxter, 1995b; Baxter, 1997]. In this paper two models of bias learning (or equivalently, learning to learn) are introduced and the main theoretical results presented. The first model is a PAC-type model based on empirical process theory, while the second is a hierarchical Bayes model.