A very common workflow is to index some data based on its embeddings and then given a new query embedding retrieve the most similar examples with k-Nearest Neighbor search. For example, you can imagine embedding a large collection of papers by their abstracts and then given a new paper of interest retrieve the most similar papers to it.
TLDR in my experience it ~always works better to use an SVM instead of kNN, if you can afford the slight computational hit
MIT 6.034 Artificial Intelligence, Fall 2010 View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In this lecture, we explore su...
In this post, I want to show how I use NLTK for preprocessing and tokenization, but then apply machine learning techniques (e.g. building a linear SVM using stochastic gradient descent) using Scikit-Learn.
In the previous post on Support Vector Machines (SVM), we looked at the mathematical details of the algorithm. In this post, I will be discussing the practical implementations of SVM for classification as well as regression. I will be using the iris dataset as an example for the classification problem, and a randomly generated data as an example for the regression problem.
S. Lahoti, S. Kayal, S. Kumbhare, I. Suradkar, и V. Pawar. 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), стр. 1-6. Aurangabad, Maharashtra, India, IEEE, (июля 2018)
T. Rezende, C. Castro, S. Almeida, и F. Guimarães. Anais do XIII Simpósio Brasileiro de Automação Inteligente, стр. 465-470. Universidade Federal do Rio Grande do Sul (UFRGS), (2017)