Search Results for "q learning algorithm" - Page 8
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Showing 277 open source projects for "q learning algorithm"
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DMTK
Microsoft Distributed Machine Learning Toolkit
The Microsoft Distributed Machine Learning Toolkit (DMTK) is an open-source framework created to support scalable machine learning across distributed computing environments. Developed by Microsoft Research, the toolkit provides infrastructure and algorithms designed to train large models efficiently on clusters of machines rather than a single system. At its core is a parameter-server architecture called Multiverso, which manages model parameters and synchronizes updates across distributed... -
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Bolt ML
10x faster matrix and vector operations
Bolt is an open-source research project focused on accelerating machine learning and data mining workloads through efficient vector compression and approximate computation techniques. The core idea behind Bolt is to compress large collections of dense numeric vectors and perform mathematical operations directly on the compressed representations instead of decompressing them first. This approach significantly reduces both memory usage and computational overhead when working with... -
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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. We've created some example projects to demonstrate the... -
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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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Random Bits Forest
RBF: a Strong Classifier/Regressor for Big Data
We present a classification and regression algorithm called Random Bits Forest (RBF). RBF integrates neural network (for depth), boosting (for wideness) and random forest (for accuracy). It first generates and selects ~10,000 small three-layer threshold random neural networks as basis by gradient boosting scheme. These binary basis are then feed into a modified random forest algorithm to obtain predictions. In conclusion, RBF is a novel framework that performs strongly especially on data... -
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nunn
This is an implementation of a machine learning library in C++17
nunn is a collection of ML algorithms and related examples written in modern C++17. -
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Algorithms in Python
Data Structures and Algorithms in Python
...Because it’s openly maintained, you can browse through issues, see test cases, and observe coding style in a “learning through code” fashion. It also serves as a playground where you can add problems, measure performance, and compare different algorithmic approaches. For anyone striving to move from “I know the syntax” to “I know how to use the right algorithm at the right time,” this repository is a practical asset. -
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...PAVT can visualize six parsing algorithms, viz. predictive parsing, simple LR parsing, canonical LR parsing, look-ahead LR parsing, Earley parsing and CYK parsing. PAVT logically explains the process of parsing showing the calculations involved in each step. The output of PAVT has been structured to maximize the learning outcomes and contains important constructs like FIRST and FOLLOW sets, item sets, parsing table, parse tree and leftmost or rightmost derivation depending on the algorithm being visualized. For instructions to use, see readme.txt.
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ingap-cdg
codon-based de Bruijn graph algorithm for gene construction
...However, these methods are of limited application due to highly fragmented transcripts and extensive assembly errors, which may lead to redundant or false CDS predictions. Here we present a novel algorithm, inGAP-CDG, for effective construction of full-length and non-redundant CDSs from unassembled transcriptomes. inGAP-CDG achieves this by combining a newly developed codon-based de bruijn graph to simplify the assembly process and a machine learning based approach to filter false positives. Compared with other methods, inGAP-CDG exhibits significantly increased predicted CDS length and robustness to sequencing errors and varied read length. -
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go-best-practice
Trying to complete over 100 projects in various categories in golang
go-best-practice is essentially a Go book and code collection called “Go 实战开发” (“Go in Practice”), born from the idea of building over 100 practical projects in different categories using Go. It combines an ebook/zh directory with written chapters and a src directory that holds the corresponding source code, so readers can move seamlessly between theory and practice. The goal is to help developers go beyond basic syntax and actually build real applications, drawing inspiration from similar... -
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Density-ratio based clustering
Discovering clusters with varying densities
This site provides the source code of two approaches for density-ratio based clustering, used for discovering clusters with varying densities. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying... -
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Attitude Estimator
A C++ implementation of a nonlinear 3D IMU fusion algorithm.
Attitude Estimator is a generic platform-independent C++ library that implements an IMU sensor fusion algorithm. Up to 3-axis gyroscope, accelerometer and magnetometer data can be processed into a full 3D quaternion orientation estimate, with the use of a nonlinear Passive Complementary Filter. The library is targeted at robotic applications, but is by no means limited to this. Features of the estimator include gyro bias estimation, transient quick learning, multiple estimation algorithms, tuneable estimator parameters, and near-global stability backed by theoretical analysis. ... -
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Sched-SPM
A C++ schedule generator based on Genetic Algorithm and Hill Climbing
...It supports automated scheduling and rescheduling. Parameters of GA such as population size, generation number, mutation probability, and crossover probability, and human factors such as learning, communication overhead, learning, and schedule pressure, could be controlled by users. -
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GA-EoC
GeneticAlgorithm-based search for Heterogeneous Ensemble Combinations
In data classification, there are no particular classifiers that perform consistently in every case. This is even worst in case of both the high dimensional and class-imbalanced datasets. To overcome the limitations of class-imbalanced data, we split the dataset using a random sub-sampling to balance them. Then, we apply the (alpha,beta)-k feature set method to select a better subset of features and combine their outputs to get a consolidated feature set for classifier training. To... -
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Adaptive Difficulty Chinese Chess
A Chinese chess game including an adaptive computer opponent.
This project is an application of POSM algorithm on Chinese chess computer player. A computer player is implemented, which will adapt to the its opponent by adjusting its playing strength accordingly. -
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GI-ICA
Matlab implementation of GI-ICA and PEGI
This is a matlab implementation of the GI-ICA algorithm for ICA in the presence of an additive Gaussian noise. The algorithm is discussed in the paper "Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis" by James Voss, Luis Rademacher, and Mikhail Belkin. -
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classify-20-NG-with-4-ML-Algo
Problem involves classifying 20000 messages into different 20 classes
...Out of all the methods, SVM using the Libsvm [1] produced the most accurate and optimized result for its classification accuracy for the 20 classes. All the algorithm implementation was written Matlab. Download the code and Report here. -
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WiiGesture for Fex/AIR
Allows inclusion of Gesture Interaction via WiiMote in Web-Apps
WiiGesture is a library to add gestural interaction in web-applications that are based on Adobe Flash/Flex/Air. Therefore it uses the WiiFlash service (http://wiiflash.bytearray.org/?page_id=50) to retrieve the sensor data and an internal algorithm based on template matching recognizes performed gestures in real-time. For easier gesture training, a so called WiiGesture-Learning desktop tool was developed that automatically generates gesture templates by performing gestures with at least one repetition. The result is an XML file that can be exported and included in the WiiGesture-API so that trained gestures are known to the API. ... -
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neurochess
Self chess learning process by artificial neural network
Self chess learning process by artificial neural network white random attribution to of values of chess game, before to pass opposed value to neural network and the desire successor of board with various part of game chess generated with minimax algorithm. -
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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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DE-HEoC
DE-based Weight Optimisation for Heterogeneous Ensemble
We propose the use of Differential Evolution algorithm for the weight adjustment of base classifiers used in weighted voting heterogeneous ensemble of classifier. Average Matthews Correlation Coefficient (MCC) score, calculated over 10-fold cross-validation, has been used as the measure of quality of an ensemble. DE/rand/1/bin algorithm has been utilised to maximize the average MCC score calculated using 10-fold cross-validation on training dataset. The voting weights of base classifiers are... -
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ExSTraCS
Extended Supervised Tracking and Classifying System
This advanced machine learning algorithm is a Michigan-style learning classifier system (LCS) developed to specialize in classification, prediction, data mining, and knowledge discovery tasks. Michigan-style LCS algorithms constitute a unique class of algorithms that distribute learned patterns over a collaborative population of of individually interpretable IF:THEN rules, allowing them to flexibly and effectively describe complex and diverse problem spaces. ... -
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JAABA
The Janelia Automated Animal Behavior Annotator
...JAABA uses machine learning techniques to convert these manual labels into behavior detectors that can then be used to automatically classify the behaviors of animals in large data sets with high throughput. JAABA combines an intuitive graphical user interface, a fast and powerful machine learning algorithm, and visualizations of the classifier into an interactive, usable system for creating automatic behavior detectors. -
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ObjectDetector
Car Detection,Face Detectiom,Object Detection
Machine learning: This project is used for training new object like Car,Motor Cycle and so on and we use this model(xml file) for detecting in images.In this project we use viola jones algorithm.