Search Results for "learning vector quantization" - Page 6
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Showing 149 open source projects for "learning vector quantization"
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Swift AI
The Swift machine learning library
Swift AI is a high-performance deep learning library written entirely in Swift. We currently offer support for all Apple platforms, with Linux support coming soon. Swift AI includes a collection of common tools used for artificial intelligence and scientific applications. A flexible, fully-connected neural network with support for deep learning. Optimized specifically for Apple hardware, using advanced parallel processing techniques. -
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node2vec
Learn continuous vector embeddings for nodes in a graph using biased R
The node2vec project provides an implementation of the node2vec algorithm, a scalable feature learning method for networks. The algorithm is designed to learn continuous vector representations of nodes in a graph by simulating biased random walks and applying skip-gram models from natural language processing. These embeddings capture community structure as well as structural equivalence, enabling machine learning on graphs for tasks such as classification, clustering, and link prediction. ... -
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Retrieval-Based Conversational Model
Dual LSTM Encoder for Dialog Response Generation
Retrieval-Based Conversational Model in Tensorflow is a project implementing a retrieval-based conversational model using a dual LSTM encoder architecture in TensorFlow, illustrating how neural networks can be trained to select appropriate responses from a fixed set of candidate replies rather than generate them from scratch. The core idea is to embed both the conversation context and potential replies into vector representations, then score how well each candidate fits the current dialogue,... -
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Netvlad
NetVLAD: CNN architecture for weakly supervised place recognition
NetVLAD is a deep learning-based image descriptor framework developed by Relja Arandjelović for place recognition and image retrieval. It extends standard CNNs with a trainable VLAD (Vector of Locally Aggregated Descriptors) layer to create compact, robust global descriptors from image features. This implementation includes training code and pretrained models using the Pittsburgh and Tokyo datasets. -
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GloVe
GloVe model for distributed word representation
GloVe is an unsupervised learning algorithm for obtaining vector representations for words. Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and the resulting representations showcase interesting linear substructures of the word vector space. The links provided contain word vectors obtained from the respective corpora. -
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classify-20-NG-with-4-ML-Algo
Problem involves classifying 20000 messages into different 20 classes
This classification problem involves classifying 20000 messages into 20 different classes. The dataset can be found here: https://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups. Four Machine Learning algorithms; Naïve Bayes, Logistic Regression, Regularized Logistic Regression Support Vector Machine (SVM) were implemented and there training and test dataset accuracy were compared. Arguably, one of the most important aspect to solving this problem is having the appropriate data set format. Each of these algorithms has its peculiar data format; the specific format and how to reconstruct the entire dataset are illustrated in other sections below. ... -
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Machine Learning framework in Python
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KMeansAniX
Animation of kmeans clustering using X Window System
Open source animation of kmeans clustering in X Window System using the C++ libplotter library. Supports Linux, Mac, and BSD. Includes common initialization methods such as Forgy, Macqueen, random, and angular. Sample videos are available through the Files Tab above. The SVN repo is accessible thorugh the Code Tab above. Requires a C++ compiler, libplot-dev, and libncurses5-dev Mac alternative to libplot-dev: macports plotutils +x11 -
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BudgetedSVM
BudgetedSVM: A C++ Toolbox for Large-scale, Non-linear Classification
We present BudgetedSVM, a C++ toolbox containing highly optimized implementations of three recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines (AMM), Budgeted Stochastic Gradient Descent (BSGD), and Low-rank Linearization SVM (LLSVM). BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, as it allows solving highly non-linear classi fication problems with millions of... -
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Multilinear Subspace Learning
MSL: Dimensionality Reduction of Tensor Data via Subspace Learning
This project aims to host multilinear subspace learning (MSL) algorithms for dimensionality reduction of multidimensional data through learning a low-dimensional subspace from tensorial representation directly. The origin of MSL traces back to multi-way analysis in the 1960s and they have been studied extensively in face and gait recognition. With more connections revealed and analogies drawn between multilinear algorithms and their linear counterparts, MSL has become an exciting area to explore for applications involving large-scale multidimensional (tensorial) data as well as a challenging problem for machine learning researchers to tackle. ... -
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SRM2 Sound Recognizer Mobile 2
A Sound Recognition Framework developed to J2ME plataform
The SRM Framework 2 was developed for the J2ME platform supporting sound recognition dependent and speaker-independent, through the recognition techniques DTW (Dynamic Time Warping) and HMM (Hidden Markov Models), in addition to providing support to the import and export of data any stage of recognition enabling the use of external resources as well as the tool running steps on remote terminals. It has the following characteristics: - DTW - HMM (Discrete Models) - Suports Sound in PCM - Vector quantization using the k-means algorithm - Uses the algorithm Forward - Uses the algorithm Backward - Uses the Viterbi algorithm - Uses the Baum-Welch algorithm for training - Has features import and export data Further information is on Gaita 2012. -
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Swarm Wars
Safety in numbers.
...They can select mates, and they can gather and distribute food and material. This behavior is controlled by a genetically evolved neural net augmented with online back propagation learning. The back propagation learning uses a reward vector and plasticity matrix that is evolved as part of the genome. Long story short, the AI is pretty frickin' sophisticated. Players can take control of organisms, trade resources and organisms in a market, and aid evolution by selective breeding. -
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Stanford Machine Learning Course
machine learning course programming exercise
The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. -
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This project contains weka packages of neural networks algorithms implementations like Learning Vector Quantizer (LVQ) and Self-organizing Maps (SOM). For more information about weka, please visit http://www.cs.waikato.ac.nz/~ml/weka/
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ssSVMToolbox is a Java application capable of performing supervised and semi-supervised learning tasks with Support Vector Machines. It is based on Spring (http://springframework.org/), RapidMiner (http://www.rapidminer.com) and Eclipse RCP (http://eclip
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SVM# is a svm(support vector machine) classification implemented in C#. The project contains both train and predict modules.
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libit provides easy to use yet efficient tools for C for signal processing, coding, or scientific code in general. It includes basic vector, matrix and function types, some common source and channel coding tools such as quantization, entropy coding, etc.
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A Python class library of tools for learning agents, including reinforcement learning algorithms, function approximators, and vector quantizations algorithms. (Pronounced "plastic".)
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The simpleSVM project contains Machine Learning codes for algorithms based on the SimpleSVM. It provides methods for Support Vector Machines and related methods, such as One-Clas SVM, nu-SVM...
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A program which retrieves TV program data and asks user which TV programs he / she'll watch that day. Using that data program learns the user preferences using machine learning algorithms (such as support vector machines).
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AutoREALM
Vector based drawing software designed for RPGs
AutoREALM is a free role-playing game mapping program originally made by Andrew Gryc. This program is an excellent mapping program that can design castles, caves, cities, dungeons and more. New developers are more than welcome! Previously, development were using Delphi language. Some attempts to rewrite it in other languages were done, but currently (since January 2012) there is an attempt or rewrite using those technologies: _ C++11 _ wxWidgets 2.9 (will be 3.0 when it will be... -
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A mini 3D computer graphics C++ cross-platform library (LGPL). Providing the minimum set of 3D objects: vector, point, transformation matrix, and quaternion. Suitable for students who are learning 3D concepts, or 3D gurus who like to build a scene-graph.
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After the presentation on CeBIT 2004 fair finally available: SpamStop works as a Proxy between your Pop3-Client and the -Provider, it uses a Support Vector Machine to classfy incoming mails and automatically learns classifiers from samples of your mails.
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This is a teaching aid for students learning the tools and techniques, such as Forte and Interval Vector Analysis, utilized in the analysis of music of the modern era, dating from the 20th century to present.
