I’ve always been curious about what makes someone “look” male or female, probably because I’m female but have never looked conventionally feminine. I was a tomboy as a child and remained one as an…
Deep Learning has revolutionised Pattern Recognition and Machine Learning. It is about credit assignment in adaptive systems with long chains of potentially causal links between actions and consequences.
Mloss is a community effort at producing reproducible research via open source software, open access to data and results, and open standards for interchange.
The aim of this project is to produce age-appropriate non-fiction books for children from birth to age 12. These books are richly illustrated with photographs, diagrams, sketches, and original drawings. Wikijunior books are produced by a worldwide community of writers, teachers, students, and young people all working together. The books present factual information that is verifiable. You are invited to join in and write, edit, and rewrite each module and book to improve its content. Our books are distributed free of charge under the terms of the Creative Commons Attribution-ShareAlike License.
Short introduction to Vector Space Model (VSM) In information retrieval or text mining, the term frequency - inverse document frequency also called tf-idf, is
This list is intended to introduce some of the tools of Bayesian statistics and machine learning that can be useful to computational research in cognitive science.
Mahout currently has
Collaborative Filtering
User and Item based recommenders
K-Means, Fuzzy K-Means clustering
Mean Shift clustering
Dirichlet process clustering
Latent Dirichlet Allocation
Singular value decomposition
Parallel Frequent Pattern mining
Complementary Naive Bayes classifier
Random forest decision tree based classifier
High performance java collections (previously colt collections)
A vibrant community
and many more cool stuff to come by this summer thanks to Google summer of code
Platform for sharing and evaluation of intelligent algorithms. Data mining data, experiments, datasets, performance analysis, data repository, challenges. Research and applications, prediction. Data mining and machine learning
The Knowledge Discovery Machine Learning (KDML) group focuses on the neighboring subfields of computer science known as knowledge discovery in databases (KDD, sometimes referred to simply as data mining) and machine learning (ML). For us, these fields include on the one hand the automated analysis of large data sets using intelligent algorithms that are capable of extracting from the collected data hidden knowledge in order to produce models that can be used for prediction and decision making. On the other hand, they also include algorithms and systems that are capable of learning from experience and adapting to their environment or their users.
Within the TENCompetence project we aim to develop and integrate models and tools into an open source infrastructure for the creation, storage and exchange of learning objects, suitable knowledge resources as well as learning experiences. This paper analyzes the potential of social software tools for providing part of the required functionality using a detailed scenario. It then discusses the challenges involved, focusing on interoperability, identity management and providing the right Web 2.0 tools for the required functionalities. Finally, we sketch a possible infrastructure based on Facebook, providing information propagation along a social network graph.
This is a repository of databases, domain theories and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms.
Q. Le, und T. Mikolov. Proceedings of the 31st International Conference on Machine Learning, Volume 32 von Proceedings of Machine Learning Research, Seite 1188--1196. Bejing, China, PMLR, (Juni 2014)
M. Ribeiro, S. Singh, und C. Guestrin. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, (August 2016)Available at https://arxiv.org/pdf/1602.04938.pdf.