kNN | BestHub

DaTaobao Tech

Mar 4, 2024 · Artificial Intelligence

Iris Classification with Machine Learning: Data Exploration and Classic Algorithms

This beginner-friendly guide walks through loading the classic Iris dataset, performing exploratory data analysis, and implementing four fundamental classifiers—Decision Tree, Logistic Regression, Support Vector Machine, and K‑Nearest Neighbors—complete with training, visualization, and accuracy evaluation, illustrating a full machine‑learning workflow.

SVMclassificationdecision tree

0 likes · 22 min read

Iris Classification with Machine Learning: Data Exploration and Classic Algorithms

Bitu Technology

Jan 17, 2024 · Artificial Intelligence

Rosetta Stone: Scalable ID Mapping System for Tubi's Content Library Using LLMs and Embeddings

This article describes how Tubi built the Rosetta Stone system—a flexible ID mapping workflow that leverages large language models, embedding similarity ranking, and K‑nearest‑neighbors to unify and enrich metadata across a 200,000‑title library, improve content recommendation, and streamline operations.

Big DataEmbeddingsLLM

0 likes · 10 min read

Rosetta Stone: Scalable ID Mapping System for Tubi's Content Library Using LLMs and Embeddings

Architects Research Society

Jul 24, 2023 · Artificial Intelligence

Neural Search in Apache Solr: Dense Vector Fields, HNSW Graphs, and K‑Nearest Neighbor Implementation

This article explains how Apache Solr implements neural search using dense vector fields, K‑Nearest Neighbor algorithms, and Hierarchical Navigable Small World graphs, detailing the underlying Lucene support, configuration options, query syntax, and integration with AI‑driven vector representations.

AIApache SolrDense Vectors

0 likes · 15 min read

Neural Search in Apache Solr: Dense Vector Fields, HNSW Graphs, and K‑Nearest Neighbor Implementation

Architects Research Society

Jun 6, 2022 · Artificial Intelligence

Neural Search in Apache Solr: Dense Vector Fields, HNSW Graphs, and K‑Nearest Neighbor Implementation

This article explains how Apache Solr and Lucene implement neural search using dense vector fields, hierarchical navigable small‑world (HNSW) graphs, and approximate K‑nearest neighbor algorithms, covering configuration, custom codecs, indexing formats, and query parsers for vector‑based retrieval.

Apache SolrDense VectorsHNSW

0 likes · 15 min read

Top Architect

Feb 21, 2022 · Databases

Key New Features in Elasticsearch 8.0

Elasticsearch 8.0 introduces major updates including 7.x REST API compatibility headers, default-enabled security with registration tokens, known issues on ARM/macOS, a preview KNN search API using dense_vector, storage reductions for keyword and text fields, faster geo indexing, PyTorch model support, and numerous other enhancements across aggregations, allocation, analysis, authentication, and core infrastructure.

Big DataElasticsearchPyTorch

0 likes · 10 min read

Laravel Tech Community

Feb 17, 2022 · Backend Development

Key New Features and Changes in Elasticsearch 8.0 Release

Elasticsearch 8.0 introduces major updates including 7.x REST API compatibility headers, default‑enabled security with registration tokens, protected system indices, a preview KNN search API, storage‑efficient keyword/match_only_text/text fields, faster indexing for geo_point and geo_shape, PyTorch model support, and numerous deprecations and enhancements across aggregations, allocation, analysis, authentication, cluster coordination, and engine components.

APIElasticsearchIndexing

0 likes · 8 min read

Key New Features and Changes in Elasticsearch 8.0 Release

Laravel Tech Community

Feb 13, 2022 · Backend Development

Key New Features and Changes in Elasticsearch 8.0 Release

Elasticsearch 8.0 introduces major updates such as 7.x REST API compatibility headers, default‑enabled security with enrollment tokens, protected system indices, a preview KNN search API, storage‑efficient field types, faster geo indexing, PyTorch model support, and numerous deprecations and bug fixes across aggregations, allocation, analysis, authentication, and core engine components.

APIElasticsearchIndexing

0 likes · 9 min read

Python Programming Learning Circle

May 7, 2020 · Artificial Intelligence

Understanding the k-Nearest Neighbor (kNN) Classification Algorithm and Its Python Implementation

This article introduces the concept and intuition behind the k-Nearest Neighbor (kNN) classification algorithm, explains its simple and full forms, discusses feature engineering and Euclidean distance calculations, and provides a complete Python implementation with example code.

Euclidean distanceFeature Engineeringclassification

0 likes · 10 min read

Understanding the k-Nearest Neighbor (kNN) Classification Algorithm and Its Python Implementation

Python Programming Learning Circle

Mar 7, 2020 · Artificial Intelligence

k-Nearest Neighbors (kNN) Algorithm: Overview, Pros/Cons, Data Preparation, Implementation, and Handwritten Digit Recognition

This article explains the k‑Nearest Neighbors classification method, discusses its advantages and drawbacks, describes data preparation and normalization, presents Python code for the algorithm and a full handwritten digit recognition project, and reports an error rate of about 1.2%.

Euclidean distancePythonclassification

0 likes · 9 min read

k-Nearest Neighbors (kNN) Algorithm: Overview, Pros/Cons, Data Preparation, Implementation, and Handwritten Digit Recognition

360 Quality & Efficiency

Aug 23, 2019 · Artificial Intelligence

High‑Performance High‑Dimensional Vector KNN Search Using FAISS

This article introduces the background of vector representations in machine learning, explains the K‑Nearest Neighbors algorithm and its key parameters, reviews traditional tree‑based and modern high‑performance search solutions, and demonstrates how FAISS can achieve microsecond‑level KNN queries on large‑scale high‑dimensional data.

Vector Searchfaisshigh-dimensional

0 likes · 5 min read

High‑Performance High‑Dimensional Vector KNN Search Using FAISS

JD Tech

Feb 12, 2019 · Artificial Intelligence

Content‑Based Filtering: Concepts, Implementation, and Pros/Cons

The article explains content‑based filtering for recommendation systems, covering its basic concepts, feature requirements, implementation using vector representations and cosine similarity, advantages and disadvantages, and supplementary algorithms such as k‑Nearest Neighbor, Rocchio, decision trees, linear classifiers, and Naive Bayes.

Naive BayesRocchiocontent-based filtering

0 likes · 11 min read

Content‑Based Filtering: Concepts, Implementation, and Pros/Cons

Qunar Tech Salon

Mar 15, 2015 · Artificial Intelligence

Overview of Common Classification Algorithms in Data Mining

This article introduces the concepts of classification and prediction in data mining, outlines their workflow, and provides concise explanations of six widely used classification techniques—decision trees, K‑Nearest Neighbour, Support Vector Machine, Vector Space Model, Bayesian methods, and neural networks—highlighting their principles, advantages, and limitations.

Data MiningSVMbayesian

0 likes · 9 min read

Overview of Common Classification Algorithms in Data Mining