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.
This page is devoted to learning methods building on kernels, such as the support vector machine. It grew out of earlier pages at the Max Planck Institute for Biological Cybernetics and at GMD FIRST, snapshots of which can be found here and here. In those days, information about kernel methods was sparse and nontrivial to find, and the kernel machines web site acted as a central repository for the field. It included a list of people working in the field, and online preprints of most publications.
Nowadays, this no longer makes sense, partly because the field is very popular, so there are too many people and papers to make such lists useful, and partly because search engines do the job much more conveniently. But what really forced us to do a major update of the site was the fact that spammers discovered our site, and it was no longer possible to operate a system which was built on the trust that people who submit an entry do so to improve the quality of the site.
They use diffuse reflection, on the equatorial region of the egg (to avoid the air sack).
"The NIR spectra were collected in the reflectance mode by using an Antaris II near-infrared spectrophotometer (Thermo Electron Co., USA) with a fiber optic sampling probe. A fiber bundle was used to illuminate the sample and collect the diffusely scattered light. The fiber probe was placed directly to contact with equatorial region of the eggshell, because the internal composition changes are more easily explored in the equatorial region rather than the two sides. Particularly, the air cell contained in the blunt end will greatly effect the spectra collection. In order to avoid possible effects due to differences in the internal composition, the diffuse reflectance spectrum was obtained by averaging three measurements carried out round the equatorial region of eggshell. Each spectrum was the average of 32 scanning spectra. The range of spectra was from 10,000 to 4000 cm−1, and the data were measured in 3.856 cm−1 interval, which resulted in 1557 variables. The temperature was kept around 25 °C and the humidity was kept at a steady level in the laboratory."
Rest of the article focuses on the use of support vector machines as a solution for the problem of having many examples of a single target class (fresh eggs) and only a few examples of an outlier class (unfresh eggs). Their method of spoiling eggs resulted in 66 fresh eggs and only 5 unfresh eggs.
LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM.
Main features of LIBLINEAR include
* Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
* Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
* Cross validation for model selection
* Probability estimates (logistic regression only)
* Weights for unbalanced data
* MATLAB/Octave, Java interfaces
This web page provides information, errata, as well as about a third of the chapters of the book Learning with Kernels, written by Bernhard Schölkopf and Alex Smola (MIT Press, Cambridge, MA, 2002).
SVM-JAVA, developed for research and educational purpose, is a Java implementation of John C. Platt's sequential minimal optimization (SMO) for training a support vector machine (SVM). This program is based on the pseudocode in "Fast Training of Support Vector Machines using Sequential Minimal Optimization" by John C. Platt and in "Sequential Minimal Optimization for SVM" by Xianping Ge. It currently supports linear and RBF kernels.
This software is an extension of the SVMlight software. It provides an interface to kernel functions that are implemented in Java by means of the Java Native Interface (JNI) Invocation API.
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM ). It supports multi-class classification.
Als Praxisanteil am Seminar Mustererkennung mit Support-Vektor-Maschinen (SVM) im WS 01/02 und WS 02/03 sind einige Wettbewerbe entstanden, die nicht nur auf diese Veranstaltung begrenzt sind. Es werden jederzeit Lösungen auch von nicht-Seminarteilnehmern angenommen. Die besten Resultate werden auf dieser Seite aktuell gehalten.
T. Evgeniou, und M. Pontil. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, Seite 109--117. (2004)
S. Kiritchenko, X. Zhu, C. Cherry, und S. Mohammad. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Seite 437--442. Dublin, Ireland, Association for Computational Linguistics, (августа 2014)
P. Molchanov, S. Gupta, K. Kim, und J. Kautz. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Seite 1-7. IEEE, (сентября 2015)