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SVM Substitute Algos

by akshara Updated: Jun 13, 2022

Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The algorithm measures its accuracy through the loss function, adjusting until the error has been sufficiently minimized. It is a process of providing input data as well as correct output data to the machine learning model. The aim of a supervised learning algorithm is to find a mapping function to map the input variable(x) with the output variable(y).

Elements in Supervised Learning : Classification uses an algorithm to accurately assign test data into specific categories. Regression is used to understand the relationship between dependent and independent variables. What is SVM? The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane. SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.

Support Vector Machine

Libraries that can be used for SVM are:

misvmby garydoranjr

Python star image 198 Version:Current

License: Permissive (BSD-3-Clause)

Multiple-Instance Support Vector Machines

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misvmby garydoranjr

Python star image 198 Version:Current License: Permissive (BSD-3-Clause)

Multiple-Instance Support Vector Machines
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jlibsvmby davidsoergel

Java star image 121 Version:Current

License: Others (Non-SPDX)

Efficient training of Support Vector Machines in Java

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jlibsvmby davidsoergel

Java star image 121 Version:Current License: Others (Non-SPDX)

Efficient training of Support Vector Machines in Java
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cnn-svmby AFAgarap

Python star image 184 Version:v0.1.0-alpha

License: Permissive (Apache-2.0)

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification

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cnn-svmby AFAgarap

Python star image 184 Version:v0.1.0-alpha License: Permissive (Apache-2.0)

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification
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Other Classification Techniques are :

Libraries are listed for each classification technique.

Naive Bayes

Naive Bayes is a probabilistic classifier, which means it predicts on the basis of the probability of an object. It is mainly used in text classification that includes a high-dimensional training dataset.

Java-Naive-Bayes-Classifierby ptnplanet

Java star image 292 Version:1.0.7

License: No License (null)

A java classifier based on the naive Bayes approach complete with Maven support and a runnable example.

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Java-Naive-Bayes-Classifierby ptnplanet

Java star image 292 Version:1.0.7 License: No License

A java classifier based on the naive Bayes approach complete with Maven support and a runnable example.
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bayesby ttezel

JavaScript star image 526 Version:Current

License: No License (null)

Naive-Bayes Classifier for node.js

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bayesby ttezel

JavaScript star image 526 Version:Current License: No License

Naive-Bayes Classifier for node.js
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nbayesby oasic

Ruby star image 147 Version:Current

License: Permissive (MIT)

A robust, full-featured Ruby implementation of Naive Bayes

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nbayesby oasic

Ruby star image 147 Version:Current License: Permissive (MIT)

A robust, full-featured Ruby implementation of Naive Bayes
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K-Nearest Neighbor

K-NN algorithm assumes the similarity between the new data and available data and put the new data into the category that is most similar to the available categories. K-NN algorithm stores all the available data and classifies a new data point based on the similarity.

kgraphby aaalgo

C++ star image 305 Version:Current

License: Permissive (BSD-2-Clause)

A library for k-nearest neighbor search

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kgraphby aaalgo

C++ star image 305 Version:Current License: Permissive (BSD-2-Clause)

A library for k-nearest neighbor search
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libnaboby ethz-asl

C++ star image 296 Version:1.0.7

License: No License (null)

A fast K Nearest Neighbor library for low-dimensional spaces

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libnaboby ethz-asl

C++ star image 296 Version:1.0.7 License: No License

A fast K Nearest Neighbor library for low-dimensional spaces
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spark-knnby saurfang

Scala star image 208 Version:Current

License: Permissive (Apache-2.0)

k-Nearest Neighbors algorithm on Spark

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spark-knnby saurfang

Scala star image 208 Version:Current License: Permissive (Apache-2.0)

k-Nearest Neighbors algorithm on Spark
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Random Forest

Random Forest is a classifier that contains a number of decision trees on various subsets of the given dataset and takes the average to improve the predictive accuracy of that dataset.

grfby grf-labs

C++ star image 781 Version:Current

License: Strong Copyleft (GPL-3.0)

Generalized Random Forests

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grfby grf-labs

C++ star image 781 Version:Current License: Strong Copyleft (GPL-3.0)

Generalized Random Forests
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rangerby imbs-hl

C++ star image 707 Version:v0.13.1

License: No License (null)

A Fast Implementation of Random Forests

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rangerby imbs-hl

C++ star image 707 Version:v0.13.1 License: No License

A Fast Implementation of Random Forests
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thundergbmby Xtra-Computing

C++ star image 598 Version:0.3.2

License: Permissive (Apache-2.0)

ThunderGBM: Fast GBDTs and Random Forests on GPUs

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thundergbmby Xtra-Computing

C++ star image 598 Version:0.3.2 License: Permissive (Apache-2.0)

ThunderGBM: Fast GBDTs and Random Forests on GPUs
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Decision Tree

Decision Tree is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome.

catboostby catboost

C star image 6893 Version:1.1.1

License: Permissive (Apache-2.0)

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

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catboostby catboost

C star image 6893 Version:1.1.1 License: Permissive (Apache-2.0)

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
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dtreevizby parrt

Jupyter Notebook star image 2408 Version:2.0.0

License: Permissive (MIT)

A python library for decision tree visualization and model interpretation.

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dtreevizby parrt

Jupyter Notebook star image 2408 Version:2.0.0 License: Permissive (MIT)

A python library for decision tree visualization and model interpretation.
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CloudForestby ryanbressler

Go star image 685 Version:Current

License: Others (Non-SPDX)

Ensembles of decision trees in go/golang.

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CloudForestby ryanbressler

Go star image 685 Version:Current License: Others (Non-SPDX)

Ensembles of decision trees in go/golang.
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