Sequential Data Item-Related Prediction Task
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A Sequential Data Item-Related Prediction Task is a sequence learning task that predicts an element of a sequential data (a sequence dataset) based on preceding elements.
- Context:
- Input: Sequence Data.
- output: Prediction.
- It can range from being a Heuristic Sequential Data Prediction Task to being a Data-Centric Sequential Data Prediction 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 Temporal Prediction Task, such as future demand prediction task.
- a Text-Item Related Prediction Task, such as: Automated Part-of-Speech Tagging.
- a DNA Segment Label Prediction Task.
- a Protein Folding Prediction Task.
- a Next Sequence Token Prediction Task, such as a word/token-level language modeling.
- …
- Counter-Example(s):
- See: Sequence Learning Task, Anomaly Prediction Task.
References
2019
- (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Sequence_learning#Sequence_learning_problems Retrieved:2019-2-24.
- 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 the preceding elements...
- 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.
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); (...).