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Logo Indefinite Core Vector Machine 0.1

by fmschleif - January 5, 2018, 22:35:38 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1343 views, 343 downloads, 3 subscriptions

About: Armadillo/C++ implementation of the Indefinite Core Vector Machine

Changes:

Some tiny errors in the Nystroem demo scripts - should be ok now Initial Announcement on mloss.org.


About: An open-source framework for benchmarking of feature selection algorithms and cost functions.

Changes:

Initial Announcement on mloss.org.


Logo Multi Annotator Supervised LDA for classification 1.0

by fmpr - January 16, 2017, 18:01:36 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1986 views, 334 downloads, 3 subscriptions

About: MA-sLDAc is a C++ implementation of the supervised topic models with labels provided by multiple annotators with different levels of expertise.

Changes:

Initial Announcement on mloss.org.


Logo opusminer 0.1-0

by opusminer - February 23, 2017, 01:01:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1915 views, 333 downloads, 3 subscriptions

About: The new R package opusminer provides an R interface to the OPUS Miner algorithm (implemented in C++) for finding the key associations in transaction data efficiently, in the form of self-sufficient itemsets, using either leverage or lift.

Changes:

Initial Announcement on mloss.org.


Logo r-cran-effects 4.0-0

by r-cran-robot - September 14, 2017, 00:00:00 CET [ Project Homepage BibTeX Download ] 1300 views, 324 downloads, 1 subscription

About: Effect Displays for Linear, Generalized Linear, and Other Models

Changes:

Fetched by r-cran-robot on 2018-01-01 00:00:07.810965


Logo Spectra. A Library for Large Scale Eigenvalue Problems 0.5.0

by yixuanq - September 13, 2017, 02:34:21 CET [ Project Homepage BibTeX Download ] 1015 views, 312 downloads, 2 subscriptions

About: A header-only C++ library for solving large scale eigenvalue problems

Changes:

Initial Announcement on mloss.org.


About: A non-iterative learning method for one-layer (no hidden layer) neural networks, where the weights can be calculated in a closed-form manner, thereby avoiding low convergence rate and also hyperparameter tuning. The proposed learning method, LANN-SVD in short, presents a good computational efficiency for large-scale data analytic.

Changes:

Initial Announcement on mloss.org.


About: A non-iterative, incremental and hyperparameter-free learning method for one-layer feedforward neural networks without hidden layers. This method efficiently obtains the optimal parameters of the network, regardless of whether the data contains a greater number of samples than variables or vice versa. It does this by using a square loss function that measures errors before the output activation functions and scales them by the slope of these functions at each data point. The outcome is a system of linear equations that obtain the network's weights and that is further transformed using Singular Value Decomposition.

Changes:

Initial Announcement on mloss.org.


Logo Bagging PCA Hashing 1.0

by openpr_nlpr - February 6, 2017, 10:38:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2026 views, 299 downloads, 3 subscriptions

About: The proposed hashing algorithm leverages the bootstrap sampling idea and integrates it with PCA, resulting in a new projection method called Bagging PCA Hashing.

Changes:

Initial Announcement on mloss.org.


About: Hierarchical Recurrent Neural Network for Skeleton Based Action Recognition

Changes:

Initial Announcement on mloss.org.


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