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In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant : "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification. In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to surpass many previous approaches in performance. Machine learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture, and medicine, where it is too costly to develop algorithms to perform the needed tasks.
Yandex LLC is a Russian multinational technology company providing Internet-related products and services, including an Internet search engine called Yandex Search, launched in 1997, information services, e-commerce, transportation, maps and navigation, mobile applications, and online advertising. Yandex Holding Company was incorporated in 2000. As of 2016, it primarily served audiences in Russia, Kazakhstan, Belarus, Turkey, and countries with significant Russian-speaking population.
Learning to rank or machine-learned ranking (MLR) is the application of machine learning, typically supervised, semi-supervised or reinforcement learning, in the construction of ranking models for information retrieval systems. Training data may, for example, consist of lists of items with some partial order specified between items in each list. This order is typically induced by giving a numerical or ordinal score or a binary judgment for each item. The goal of constructing the ranking model is to rank new, unseen lists in a similar way to rankings in the training data.
Gradient boosting is a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals rather than the typical residuals used in traditional boosting. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest. A gradient-boosted trees model is built in a stage-wise fashion as in other boosting methods, but it generalizes the other methods by allowing optimization of an arbitrary differentiable loss function.
In network theory, link analysis is a data-analysis technique used to evaluate relationships between nodes. Relationships may be identified among various types of nodes (100k), including organizations, people and transactions. Link analysis has been used for investigation of criminal activity, computer security analysis, search engine optimization, market research, medical research, and art.
Arkady Yuryevich Volozh is a Russian billionaire and businessman, serial technology entrepreneur, computer scientist, investor and philanthropist. He pioneered the development of search and navigation technology as well as intelligent products and services powered by machine learning. Volozh co-founded several IT enterprises, including CompTek, Arkadia, InfiNet and Yandex. Yandex is one of Europe's largest Internet companies, operating Russia's most popular search engine. Yandex is listed in NASDAQ, and was traded at over 30 billion dollars in November 2021.
Yandex Search is a search engine. It is owned by Yandex, based in Russia. In January 2015, Yandex Search generated 51.2% of all of the search traffic in Russia according to LiveInternet.
Kaggle is a data science competition platform and online community of data scientists and machine learning practitioners under Google LLC. Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Vowpal Wabbit (VW) is an open-source fast online interactive machine learning system library and program developed originally at Yahoo! Research, and currently at Microsoft Research. It was started and is led by John Langford. Vowpal Wabbit's interactive learning support is particularly notable including Contextual Bandits, Active Learning, and forms of guided Reinforcement Learning. Vowpal Wabbit provides an efficient scalable out-of-core implementation with support for a number of machine learning reductions, importance weighting, and a selection of different loss functions and optimization algorithms.
Quantum machine learning is the integration of quantum algorithms within machine learning programs.
Yandex Data Factory (YDF) is a B2B division of Yandex, the leading Russian search engine and one of the largest European internet companies.
The following outline is provided as an overview of and topical guide to machine learning:
Zen is a personal recommendations service created by Yandex that uses machine learning technology.
ML.NET is a free software machine learning library for the C# and F# programming languages. It also supports Python models when used together with NimbusML. The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. Additional ML tasks like anomaly detection and recommendation systems have since been added, and other approaches like deep learning will be included in future versions.
LightGBM, short for light gradient-boosting machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and scalability.
CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which among other features attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. It works on Linux, Windows, macOS, and is available in Python, R, and models built using catboost can be used for predictions in C++, Java, C#, Rust, Core ML, ONNX, and PMML. The source code is licensed under Apache License and available on GitHub.
References
- ↑ MatrixNet: New Level of Search Quality. Retrieved 2015-12-24.
- ↑ Yandex Boosts Precision Ad Targeting; Machine-Learning Method MatrixNet Is Behind The Scenes. Retrieved 2015-12-24.
- ↑ CERN boosts its search for antimatter with Yandex’s MatrixNet search engine tech. Retrieved 2015-12-24.
