Sequence Learning Task
A Sequence Learning Task is a structured learning task that accepts a sequence.
- Context:
- It can be solved by a Sequence Learning System (that implements a Sequence Learning algorithm).
- It can range from being a Unsupervised Sequence Learning Task, to being a Semi-Supervised Sequence Learning Task, to being a Supervised Sequence Learning Task.
- It can range from being an Explicit Sequence Learning Task to being an Implicit Sequence Learning Task.
- It can range from being a Heuristic Sequential-Data Learning Task to being a Data-Centric Sequential-Data Learning Task (such as supervised sequential data classification).
- It can range from being a Sequential Data Point Prediction Task to being a Sequential Data Series Prediction Task (such as sequential data pattern classification).
- It can range from being a Sequential Data Point Estimation Task to being a Sequential Data Ranking Task to being a Sequential Data Classification Task.
- It can be solved by a Sequential Data Prediction System (by applying a sequential data prediction algorithm).
- …
- Example(s):
- a Sequential Decision-Making Task;
- a Sequence Labeling Task, such as part-of-speech tagging.
- a Sequence Prediction Task;
- a Sequence Generation Task;
- a Sequence Recognition Task;
- an Implicit Sequence Learning Task such as:
- a Neural Sequence Learning Task such as:
- …
- Counter-Example(s):
- See: Complex Input Classification Task, Sentiment Analysis, LSTM, Recurrent Neural Network, Associative Reinforcement Learning, Active Learning.
References
2013a
- (Wikipedia, 2013) ⇒ http://en.wikipedia.org/wiki/Sequence_learning#Sequence_learning_problems
- Sequence learning problems are used to better understand the different types of sequence learning. There are four basic sequence learning problems: sequence prediction, sequence generation, sequence recognition, and sequential decision making. These “problems” show how sequences are formulated. They show the patterns sequences follow and how these different sequence learning problems are related to each other.
Sequence prediction attempts to predict the next immediate element of a sequence based on all of the preceding elements. Sequence generation is basically the same as sequence prediction: an attempt to piece together a sequence one by one the way it naturally occurs. Sequence recognition takes certain criteria and determines whether or not the sequence is legitimate. Sequential decision making or sequence generation through actions breaks down into three variations: goal-oriented, trajectory-oriented, and reinforcement-maximizing. These three variations all want to pick the action(s) or step(s) that will lead to the goal in the future.[1]
These sequence learning problems reflect hierarchical organization of plans because each element in the sequences builds on the previous elements.
In a classic experiment published in 1967, Alfred L. Yarbus demonstrated that though subjects viewing portraits reported apprehending the portrait as a whole, their eye movements successively fixated on the most informative parts of the image. These observations suggest that underlying an apparently parallel process of face perception, a serial oculomotor process is concealed.[2] It is a common observation that when a skill is being acquired, we are more attentive in the initial phase, but after repeated practice, the skill becomes nearly automatic;[3] this is also known as unconscious competence. We can then concentrate on learning a new action while performing previously learned actions skillfully. Thus it appears that a neural code or representation for the learned skill is created in our brain, which is usually called procedural memory. The procedural memory encodes procedures or algorithms rather than facts.
- Sequence learning problems are used to better understand the different types of sequence learning. There are four basic sequence learning problems: sequence prediction, sequence generation, sequence recognition, and sequential decision making. These “problems” show how sequences are formulated. They show the patterns sequences follow and how these different sequence learning problems are related to each other.
2013b
- (Wikipedia, 2013) ⇒ http://en.wikipedia.org/wiki/Sequence_learning
- Sequence learning is inherent to human ability because it is an integrated part of conscious and nonconscious learning as well as activities. Sequences of information or sequences of actions are used in various everyday tasks: "from sequencing sounds in speech, to sequencing movements in typing or playing instruments, to sequencing actions in driving an automobile."[4] Sequence learning can be used to study skill acquisition and in studies of various groups ranging from neuropsychological patients to infants. According to Ritter and Nerb, “The order in which material is presented can strongly influence what is learned, how fast performance increases, and sometimes even whether the material is learned at all.”[5] Sequence learning, more known and understood as a form of explicit learning, is now also being studied as a form of implicit learning as well as other forms of learning. Sequence learning can also be referred to as sequential behavior, behavior sequencing, and serial order in behavior.
- ↑ Sun, Ron. "Introduction to Sequence Learning". http://www.cogsci.rpi.edu/~rsun/sun.seq-intro.ps. Retrieved 30 June 2011.
- ↑ Yarbus, Alfred L., "Eye movements during perception of complex objects", Yarbus, Alfred L., tr. Basil Haigh, ed. Lorrin A. Riggs, Eye Movements and Vision, New York: Plenum, 1967, OCLC 220267263, ch. 7, pp. 171–96.
- ↑ Fitts, P. M., "Perceptual motor skill learning", in Arthur W. Melton (ed.), Categories of Human Learning, New York: Academic Press, 1964, OCLC 180195, pp. 243–85.
- ↑ Clegg, Benjamin A; DiGirolamo, Gregory J, Keele, Steven W (August 1998). "Sequence learning". Trends in Cognitive Sciences 2 (8): 275–81. doi:10.1016/S1364-6613(98)01202-9.
- ↑ Frank E. Ritter et al., ed. (2007). In order to learn: how the sequence of topics influences learning. Oxford series on cognitive models and architectures. Oxford/New York: Oxford University Press. ISBN 978-0-19-517884-5.
2001
- (Sun, 2001) ⇒ Ron Sun. (2001). “Introduction to Sequence Learning.” In: Sequence Learning - Paradigms, Algorithms, and Applications. ISBN:978-3-540-44565-4 doi:10.1007/3-540-44565-X_1
- QUOTE: ... With some necessary simplification, we can categorize various sequence learning problems that have been tackled into the following categories: (1) sequence prediction, in which, we want to predict elements of a sequence based on the preceding element(s): (2) sequence generation, in which we want to generate elements of a sequence one by one in their natural order: and (3) sequence recognition, in which we want to determine if a sequence is a legitimate one according to some criteria: in addition, (4) sequential decision making involves selecting sequence of actions, to accomplish a goal, to follow a trajectory, or to maximize (or minimize) a reinforcement (or cost) function that is normally the (discounted) sum of reinforcements (costs) that are received along the way (see Bellman 1957, Bertsekas and Tsitsiklis 1995) ...