Supervised learning is a type of machine learning in which data is fully labeled and algorithms learn to approximate a mapping function well enough that they can accurately predict output variables given new input data. This section contains supervised learning techniques. For example, Support Vector Machine (SVM), is a type of algorithm that is a discriminative classifier formally defined by a separating hyperplane used for regression and classification tasks.
Supervised learning is predicated on the use of well-labeled data. There are a number of companies in the data labeling software industry.
Data labeling software companies
NVIDIA Blog: Supervised Vs. Unsupervised Learning
Supervised and Unsupervised Machine Learning Algorithms
- Natural language processing (NLP)Natural language processing is a branch of artificial intelligence that is concerned with giving computers the ability to comprehend spoken words and text in the same way humans can.
- Linear regressionStatistical approach for modeling the relationship between a scalar dependent variable and one or more explanatory variables
- Random forestStatistical algorithm that is used to cluster points of data in functional groups
Leeds RisingProduct at Golden. Email me with suggestions, questions, or concerns - email@example.com
Daniel FrumkinMechanical engineering, cryptocurrencies, AI, and travel.
Jason D. RowleyFormer Data Acquisition Lead @ Golden (Recursion Inc.). Excited about open knowledge and pulling the future forward.
Henry OgollaPassionate about reading and writing.
Pete RIvettOntologist with Golden, knowledgeable about Semantic Web stack.