SIOC Post profile for "AI3:::Adaptive Information"
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primary topic: A (Partial) Taxonomy of Machine Learning Features
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A (Partial) Taxonomy of Machine Learning Features
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2015-11-23T14:40:45Z
content: Knowledge Bases Enable a More Systematic Approach to Feature Engineering Download PDF The two most labor-intensive steps in machine learning for natural language are: 1) feature engineering; and 2) labeling of training sets. Supervised machine learning uses these training sets where every point is an input-output pair, mapping an input, which is a feature, to an output, which is the label. The machine learning consists of inferring ("learning") a function that maps between these inputs and outputs with acceptable predictive power. This learned function can then be applied to previously unseen inputs in order to predict the output label. The technique is particularly suited to problems of regression or of classification. It is not surprising that the two most labor-intensive tasks in machine learning involve determining the suitable inputs (features) and correctly labeling the output training labels. Elsewhere in this series I discuss training sets and labeling in detail. For this current article, we focus on features. "Features" are perhaps the least discussed aspect of machine learning. References are made to how to select them; how to construct, extract or learn them; or how even to overall engineer them. But little guidance is provided as to what features exactly are. There really is no listing or inventory for what "features" might even be considered in the various aspects of natural language or text understanding. In part, I think, because we do not have this concrete feel for features, we also don't tend to understand how to maximize and systematize the role of features in support of our learning tasks. This aspect provides a compelling rationale for the advantages of properly constructed knowledge bases in support of artificial intelligence, what we have been terming as KBAI in this series. So, before we can understand how to best leverage features in our KBAI efforts, we need to first define and name the feature space. That effort, in turn, enables us to pro…
encoded: Knowledge Bases Enable a More Systematic Approach to Feature Engineering Download PDF The two most labor-intensive steps in machine learning for natural language are: 1) feature engineering; and 2) labeling of training sets. Supervised machine learning uses these training sets where every point is an input-output pair, mapping an input, which is a feature, to an output, which is the label. The machine learning consists of inferring (“learning”) a function that maps between these inputs and outputs with acceptable predictive power. This learned function can then be applied to previously unseen inputs in order to predict the output label. The technique is particularly suited to problems of regression or of classification. It is not surprising that the two most labor-intensive tasks in machine learning involve determining the suitable inputs (features) and correctly labeling the output training labels. Elsewhere in this series I discuss training sets and labeling in detail. For this current article, we focus on features. “Features” are perhaps the least discussed aspect of machine learning. References are made to how to select them; how to construct, extract or learn them; or how even to overall engineer them. But little guidance is provided as to what features exactly are. There really is no listing or inventory for what “features” might even be considered in the various aspects of natural language or text understanding. In part, I think, because we do not have this concrete feel for features, we also don’t tend to understand how to maximize and systematize the role of features in support of our learning tasks. This aspect provides a compelling rationale for the advantages of properly constructed knowledge bases in support of artificial intelligence, what we have been terming as KBAI in this series. So, before we can understand how to best leverage features in our KBAI efforts, we need to first define and name the feature space. That effort, in turn, enables us to pro…
topic: Artificial Intelligence
topic: Knowledge-based Artificial Intelligence
links to: Download PDF
links to: Supervised machine learning
links to: this series
links to: features
links to: select
links to: construct
links to: extract
links to: learn
links to: engineer
links to: [1]
links to: [2]
links to: explanatory variable
links to: [3]
links to: deep learning
links to: high-dimensional
links to: [4]
links to: curse of dimensionality
links to: [5]
links to: [6]
links to: 7
links to: 8
links to: 9
links to: 10
links to: 11
links to: 12
links to: 13
links to: 14
links to: 15
links to: 16
links to: 17
links to: Autoencoders
links to: Kernels
links to: [18]
links to: [19]
links to: Structured Dynamics
links to: image here
links to: overfitting
links to: [20]
links to: 6
links to: 22
links to: [23]
links to: metalearning
links to: hyperparameter optimization
links to: model selection
links to: A Few Useful Things to Know About Machine Learning
links to: Dynamic Programming
links to: An Introduction to Feature Extraction
links to: Feature Extraction: Foundations and Applications
links to: Convolution Kernels on Discrete Structures
links to: Automatic Classifier Selection for Non-experts
links to: Feature Selection for Classification: A Review
links to: Ontology-based Meta-mining of Knowledge Discovery Workflows,
links to: Ontology of Core Data Mining Entities
links to: Supervised Learning for Linking Named Entities to Knowledge Base Entries,"
links to: Automated Feature Generation from Structured Knowledge
links to: Learning a Concept‐based Document Similarity Measure
links to: Mining Meaning from Wikipedia
links to: Wikipedia Taxonomic Relation Extraction using Wikipedia Distant Supervision
links to: Knowledge-based Artificial Intelligence
links to: SWEETpedia
links to: Wikipedia in Academic Studies
links to: Irrelevant features and the subset selection problem
links to: Feature Engineering in Machine Learning
links to: Deep Belief Networks
links to: A Fast Learning Algorithm for Deep Belief Nets

Mike Bergman
type: User

Mike Bergman
type: Person

Supervised machine learning
label: Unsupervised learning

this series
label: KBAI in this series
label: KBAI series

features
label: feature entry

overfitting
label: Overfitting

6
label: 21