X-Nico

unusual facts about Machine learning



Apprenticeship learning

Apprenticeship learning, or apprenticeship via inverse reinforcement learning (AIRP), is a concept in the field of Artificial Intelligence and Machine learning, developed by Pieter Abbeel, Assistant Professor in Berkeley's EECS department, and Andrew Ng, Associate Professor in Stanford University's Computer Science Department.

Branch predictor

Machine learning for branch prediction using LVQ and multi-layer perceptrons, called "neural branch prediction," was proposed by Prof. Lucian Vintan (Lucian Blaga University of Sibiu).

Hyperplane

Affine hyperplanes are used to define decision boundaries in many machine learning algorithms such as linear-combination (oblique) decision trees, and Perceptrons.

Louis-Philippe Morency

His main research interest is computational study of human multimodal computation, a multi-disciplinary research topic that overlays the fields of multi-modal interaction, machine learning, computer vision, social psychology and artificial intelligence.

Mehryar Mohri

Mehryar Mohri is a professor of computer science at the Courant Institute of Mathematical Sciences at New York University known for his work in machine learning, automata theory and algorithms, speech recognition and natural language processing.

Notion Capital

In 2013, Chandler, along with former Hewlett-Packard divisional chief technology officer Graham York, announced the launch of SeedCloud, a European accelerator targeting Cloud computing startups in such areas as big data and machine learning.

Sanjeev Kulkarni

Sanjeev Ramesh Kulkarni (born Mumbai, India, September 21, 1963) is Professor of Electrical Engineering at Princeton University, where he teaches and conducts research in a broad range of areas including statistical inference, pattern recognition, machine learning, information theory, and signal/image processing.

T-Distributed Stochastic Neighbor Embedding

t-Distributed Stochastic Neighbor Embedding (t-SNE) is a machine learning algorithm for dimensionality reduction developed by Laurens van der Maaten and Geoffrey Hinton.


see also

Captricity

Captricity is a data capture software program (and the company that sells it) that uses a combination of machine-learning and human verification to perform OCR data capture from hand-filled forms.

Claude Sammut

He was a member of the executive committee of the RoboCup Federation from 2003 to 2009 and was the co-editor-in-chief of Springer's Encyclopedia of Machine Learning in 2010.

Lukas Biewald

From 2005 to 2006, Biewald led the Search Relevance Team for Yahoo! Japan, where he focused on using statistical machine learning approaches to improve the web search ranking function for international markets.

Microsoft Research Labs

Microsoft Live Labs, an applied research group that focuses on internet products and services, including natural language processing, machine learning, information retrieval, data mining, computational linguistics, and distributed computing

Numerical Recipes

The Numerical Recipes books cover a range of topics that include both classical numerical analysis (interpolation, integration, linear algebra, differential equations, and so on), signal processing (Fourier methods, filtering), statistical treatment of data, and a few topics in machine learning (hidden Markov models, support vector machines).

Richard Samworth

Fan, J., Samworth, R. and Wu, Y. (2009), Ultrahigh dimensional feature selection: beyond the linear model, J. Machine Learning Research, 10, 2013—2038.

Rudolf Wille

His most celebrated work is the invention of Formal concept analysis, a supervised machine learning technique that applies mathematical lattice theory to organize data based on objects and their shared attributes.

Search space

Version space, developed via machine learning, it is the subset of all hypotheses that are consistent with the observed training examples

Skytree

Skytree, Inc, a machine learning start up based in San Jose, California, USA

Solomonov

Ray Solomonoff (1926–2009), mathematician involved in machine learning

Variational

Variational Bayesian methods, a family of techniques for approximating integrals in Bayesian inference and machine learning