Search Results for "spreadsheet machine learning" - Page 3
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Showing 65 open source projects for "spreadsheet machine learning"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Zabal6 is a machine learning student tool based on decision tree learning, focused in the area of knowledge discovery (data mining), and inspired on See5. Zabl6 is a C++ program for Linux and windows O.S, with a intuitive graphical interface.
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Machine learning toolkit for unsupervised and semi-supervised clustering that demonstrates excellent results on real-world data (see Bekkerman et al. ICML-2005 and ECML-2006).
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PCP (Pattern Classification Program) is an open-source machine learning program for supervised classification of patterns. PCP is a binary executable running on Linux and Windows (under Cygwin environment).
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MultiBoost is a C++ implementation of the multi-class AdaBoost algorithm. AdaBoost is a powerful meta-learning algorithm commonly used in machine learning. The code is well documented and easy to extend, especially for adding new weak learners.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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CompLearn is a general-purpose compression-based machine learning system that uses data compression to learn or data mine patterns in arbitrary data.
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FLPD is an automatic learning system based on fuzzy prototypes, composed of a C++ library for machine learning and fuzzy logic and an experimentation framework.
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The TreeQ package is a set of C-language applications that implement a automatic machine learning algorithm based on a tree-structured classifier. This approach is particularly effective for high-dimensional continuous data such as audio and video.
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JUDGE (Java Utility for Document Genre Eduction) features automatic classification and clustering of documents, optionally as a webservice. The program is written entirely in Java and makes use of the Weka machine learning toolkit.
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VFML -- Very Fast Machine Learning toolkit. A collection of tools, learners, and APIs for working with high-speed data streams and very large data sets.
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AI-powered service management for IT and enterprise teamsGive your IT, operations, and business teams the ability to deliver exceptional services—without the complexity. Maximize operational efficiency with refreshingly simple, AI-powered Freshservice.
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Ruby SVM is a Ruby binding to the very popular and highly useful libsvm library (released under a seperate license) This allows you to effortlessly experiment with machine learning, in particular Support Vector Machines, in Ruby. SVM's have found use in
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Weka-Parallel is a modification to Weka, created with the intention of being able to harness the power of Weka and the speed of parallel processing to be able to run a number of data mining and machine learning algorithms quickly.
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Java port and extension of MLC++ 2.0 by Kohavi et al. Currently contains ID3, C4.5, Naive (aka Simple) Bayes, and FSS and CHC (genetic algorithm) wrappers for feature selection. WEKA 3 interfaces are in development.
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R based genetic algorithm for optimization, variable selection and other machine learning and statistical analysis approaches.
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Weka++ is a collection of machine learning and data mining algorithm implementations ported from Weka (http://www.cs.waikato.ac.nz/ml/weka/) from Java to C++, with enhancements for usability as embedded components.
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ScenConnect shows scenarios as networks of situation and event tag sets, for fast comparisons. It links scenarios to tags, scores, and other metadata, creating situationals suitable for search, mining, machine learning, and planning.
