Which method applied in machine learning is the older one?
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The linear regression method based on the least squares method is considered one of the older methods applied in machine learning. This classical method is widely used for prediction problems and has been prevalent for a considerable period. In contrast, newer optimization algorithms in deep learning, such as sampled quasi-Newton methods and machine learning approaches like random forest algorithms, have emerged more recently. While linear regression based on least squares has a long history in machine learning, advancements in optimization techniques and algorithmic approaches have been developed to address more complex problems and datasets in the field.
Answers from top 4 papers
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| Papers (4) | Insight |
|---|---|
05 Sep 2011 | Not addressed in the paper. |
Open access 01 Jan 2020 | The older method applied in machine learning is the linear regression method based on the least squares method, as stated in the paper. |
12 Citations | The older method in machine learning is the classical variants of quasi-Newton methods, as the paper introduces newer sampled quasi-Newton methods like sampled LBFGS and sampled LSR1. |
6 Citations | Random forest algorithm is a common approach in machine learning applied to predict falls among older adults, as discussed in the paper. |
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