Sequence Learning

Abstract

PrefaceThis book is intended for use by scientists, engineers, and students interested in sequence learning in artificial intelligence, neural networks, and cognitive science. The book will introduce essential algorithms and models of sequence learning and develop them in various ways. With the help of these concepts, a variety of applications will be examined. This book will allow the reader to acquire an appreciation of the breadth and variety within the field of sequence learning and its potential as an interesting area of research and application. The reader is presumed to have basic knowledge of neural networks and AI concepts.Sequential behavior is essential to intelligence and a fundamental part of human activities ranging from reasoning to language, and from everyday skills to complex problem solving. Sequence learning is an important component of learning in many task domains -planning, reasoning, robotics, natural language processing, speech recognition, adaptive control, time series prediction, and so on. Naturally, there are many different approaches towards sequence learning. These approaches deal with different aspects of sequence learning. This book provides an overall framework for this field.

Serial pattern learning in pigeons: Rule-based or associative?

“…Serial pattern learning is the process of learning to organize behavior through time, typically by learning to anticipate or respond to a sequence of events or to learn to properly organize a sequence of behavior. This type of learning is likely a fundamental capacity in human and nonhuman animals (Sun & Giles, 2001b). Sequences “play a pivotal role in classical studies of instrumental conditioning, in human skill learning, and in human high-level problem solving and reasoning” (Sun & Giles, 2001a, p. 2).…”

Sequential Pattern Mining for Intrusion Detection System with Feature Selection on Big Data

“…A subsequence is called sequential pattern or frequent sequence. Frequent sequence frequently appears in a sequence database, and its frequency must be less than user-specified minimum support threshold [7] [10] [11]. The vertical algorithms to perform well on datasets having long and dense sequences and to have excellent overall performance compared with algorithms using the horizontal format [8][9][11] [12].…”