Flipboard

DECEMBER 18, 2024

The ability to quickly access relevant information is a key differentiator in todays competitive landscape. It supports advanced features such as result highlighting, flexible pagination, and k-nearest neighbor (k-NN) search for vector and semantic search use cases. Also, Cohere Rerank 3.5,

K-nearest Neighbors 121

K-nearest Neighbors AWS ML ML 121

Dataconomy

MARCH 4, 2025

Data mining refers to the systematic process of analyzing large datasets to uncover hidden patterns and relationships that inform and address business challenges. Decision trees and K-nearest neighbors (KNN) Both decision trees and KNN play vital roles in classification and prediction. What is data mining?

Data Mining 91

Data Mining Data Mining Data Mining Decision Trees 91

Towards AI

APRIL 4, 2024

A sector that is currently being influenced by machine learning is the geospatial sector, through well-crafted algorithms that improve data analysis through mapping techniques such as image classification, object detection, spatial clustering, and predictive modeling, revolutionizing how we understand and interact with geographic information.

K-nearest Neighbors 81

K-nearest Neighbors Machine Learning Machine Learning Decision Trees 81

Pickl AI

APRIL 13, 2025

Examples include: Classifying species of plants Categorizing images into animals, vehicles, or landscapes Algorithms like Random Forests, Naive Bayes, and K-Nearest Neighbors (KNN) are commonly used for multi-class classification. Each instance is assigned to one of several predefined categories.

Machine Learning 52

Machine Learning Machine Learning Decision Trees K-nearest Neighbors 52

AWS Machine Learning Blog

NOVEMBER 13, 2024

A reverse image search engine enables users to upload an image to find related information instead of using text-based queries. For more information on managing credentials securely, see the AWS Boto3 documentation. The closer vectors are to one another in this space, the more similar the information they represent is.

AWS 116

AWS Database K-nearest Neighbors AI 116

AWS Machine Learning Blog

NOVEMBER 15, 2024

The challenge here is to retrieve the relevant data source to answer the question and correctly extract information from that data source. Use cases we have worked on include: Technical assistance for field engineers – We built a system that aggregates information about a company’s specific products and field expertise.

Database 118

Database SQL Data Analysis Data Analysis 118

Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI.

Machine Learning 105

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 105

Pickl AI

JANUARY 21, 2025

Example: Determining whether an email is spam or not based on features like word frequency and sender information. k-Nearest Neighbors (k-NN) k-NN is a simple algorithm that classifies new instances based on the majority class among its k nearest neighbours in the training dataset.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Flipboard

NOVEMBER 17, 2023

MongoDB Atlas Vector Search uses a technique called k-nearest neighbors (k-NN) to search for similar vectors. k-NN works by finding the k most similar vectors to a given vector. Refer to Review knnVector Type Limitations for more information about the limitations of the knnVector type.

K-nearest Neighbors 170

K-nearest Neighbors AWS Clustering Database 170

Dataversity

JUNE 4, 2021

The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. Click to learn more about author Kartik Patel. In this article, we will discuss the KNN Classification method of analysis. What Is the KNN Classification Algorithm?

K-nearest Neighbors 52

K-nearest Neighbors Algorithm Data Analysis Data Analysis 52

AWS Machine Learning Blog

FEBRUARY 13, 2025

This is the k-nearest neighbor (k-NN) algorithm. In k-NN, you can make assumptions around a data point based on its proximity to other data points. For more information, refer to Create a VPC. You can use the embedding of an article and check the similarity of the article against the preceding embeddings.

AWS 108

AWS K-nearest Neighbors Clustering Algorithm 108

AWS Machine Learning Blog

MARCH 11, 2025

Retrieval (and reranking) strategy FloTorch used a retrieval strategy with a k-nearest neighbor (k-NN) of five for retrieved chunks. For more information, contact us at info@flotorch.ai. Each provisioned node was r7g.4xlarge, FloTorch used HSNW indexing in OpenSearch Service.

K-nearest Neighbors 108

K-nearest Neighbors AWS Database AI 108

AWS Machine Learning Blog

SEPTEMBER 8, 2023

For this post, you use the following: Name, Id, and Urls – The celebrity name, a unique Amazon Rekognition ID, and list of URLs such as the celebrity’s IMDb or Wikipedia link for further information. Using the k-nearest neighbors (k-NN) algorithm, you define how many images to return in your results.

K-nearest Neighbors 125

K-nearest Neighbors AWS ML ML 125

Towards AI

NOVEMBER 6, 2024

Evaluation allows us to select the top embedding models across various dimensions, potentially considering multiple values for k nearest neighbors. Evaluating the Retrieval Pipeline Now that we’ve found the optimal embedding model for our use case, the next step is to evaluate the retrieval pipeline itself.

K-nearest Neighbors 116

K-nearest Neighbors AI AI Machine Learning 116

Towards AI

JULY 15, 2024

We shall look at various machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can install and call their libraries in R studios, including executing the code.

Machine Learning 119

Machine Learning Machine Learning Algorithm K-nearest Neighbors 119

AWS Machine Learning Blog

SEPTEMBER 6, 2023

Realizing the impact of these applications can provide enhanced insights to the customers and positively impact the performance efficiency in the organization, with easy information retrieval and automating certain time-consuming tasks. For more information about foundation models, see Getting started with Amazon SageMaker JumpStart.

AWS 126

AWS K-nearest Neighbors AI AI 126

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? The information from previous decisions is analyzed via the decision tree.

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? The information from previous decisions is analyzed via the decision tree.

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Towards AI

APRIL 14, 2023

Adding such extra information should improve the classification compared to the previous method (Principle Label Space Transformation). The prediction is then done using a k-nearest neighbor method within the embedding space. Distance preserving embeddings: The name of this method is straightforward.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

Towards AI

APRIL 23, 2025

Vector search, also known as vector similarity search or nearest neighbor search, is a technique used in data retrieval for RAG applications and information retrieval systems to find items or data points that are similar or closely related to a given query vector. Vector Search is Not Effortless!

K-nearest Neighbors 73

K-nearest Neighbors Database ETL Machine Learning 73

Pickl AI

JULY 31, 2023

Examples of Lazy Learning Algorithms: K-Nearest Neighbors (k-NN) : k-NN is a classic Lazy Learning algorithm used for both classification and regression tasks. The algorithm identifies the k-nearest neighbors, where k is a user-defined parameter that is most similar to the new instance.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

AWS Machine Learning Blog

AUGUST 26, 2024

It combines information from various sources into comprehensive, on-demand summaries available in our CRM or proactively delivered based on upcoming meetings. Account Summaries provides a 360-degree account narrative with customizable sections, showcasing timely and relevant information about customers.

AWS 128

AWS AI AI K-nearest Neighbors 128

PyImageSearch

JUNE 19, 2023

New users may find establishing a user profile vector difficult due to limited information about their interests. Like content-based recommendations, collaborative systems have their limitations: Identifying the -closest users for new users is difficult because of the limited information about their interests.

K-nearest Neighbors 57

K-nearest Neighbors Clustering Algorithm Deep Learning 57

AWS Machine Learning Blog

APRIL 3, 2024

We detail the steps to use an Amazon Titan Multimodal Embeddings model to encode images and text into embeddings, ingest embeddings into an OpenSearch Service index, and query the index using the OpenSearch Service k-nearest neighbors (k-NN) functionality.

K-nearest Neighbors 132

K-nearest Neighbors AWS Machine Learning Machine Learning 132

AWS Machine Learning Blog

NOVEMBER 30, 2023

Do not add any information that is not mentioned in the text below.” The embedding model also has a maximum token input count, therefore summarizing the article is even more important to make sure that you can get as much information captured in the embedding as possible.

AWS 124

AWS K-nearest Neighbors ML ML 124

Mlearning.ai

FEBRUARY 2, 2023

The algorithm must balance exploring different arms to gather information about their expected reward, while also exploiting the knowledge it has gained to make decisions that are likely to result in high rewards. bag of words or TF-IDF vectors) and splitting the data into training and testing sets.

K-nearest Neighbors 52

K-nearest Neighbors Algorithm Support Vector Machines Decision Trees 52

AWS Machine Learning Blog

APRIL 19, 2024

Solution overview The solution provides an implementation for answering questions using information contained in text and visual elements of a slide deck. We perform a k-nearest neighbor (k-NN) search to retrieve the most relevant embeddings matching the user query. I need numbers. Up to 4x higher throughput.

AWS 126

AWS ML ML Database 126

AWS Machine Learning Blog

JANUARY 30, 2024

It could contain information in the form of text, or embedded in graphs, tables, and pictures. Solution overview The solution provides an implementation for answering questions using information contained in the text and visual elements of a slide deck. Setting k=1 retrieves the most relevant slide to the user question.

AWS 130

AWS ML ML K-nearest Neighbors 130

PyImageSearch

JANUARY 27, 2025

These word vectors are trained from Twitter data making them semantically rich in information. On Line 28 , we sort the distances and select the top k nearest neighbors. Citation Information Puneet Mangla. Approximate Nearest Neighbor with Locality Sensitive Hashing (LSH), PyImageSearch , P.

K-nearest Neighbors 96

K-nearest Neighbors Algorithm Data Preparation Database 96

Pickl AI

JULY 17, 2023

Significantly, the technique allows the model to work independently by discovering its patterns and previously undetected information. It aims to partition a given dataset into K clusters, where each data point belongs to the cluster with the nearest mean. Therefore, it mainly deals with unlabelled data.

Machine Learning 52

Machine Learning Machine Learning Clustering K-nearest Neighbors 52

IBM Journey to AI blog

DECEMBER 19, 2023

Common machine learning algorithms for supervised learning include: K-nearest neighbor (KNN) algorithm : This algorithm is a density-based classifier or regression modeling tool used for anomaly detection. These equations use labeled and unlabeled data to predict future outcomes when only some of the information is known.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

AWS Machine Learning Blog

DECEMBER 6, 2023

This method of enriching the LLM generation context with information retrieved from your internal data sources is called Retrieval Augmented Generation (RAG), and produces assistants that are domain specific and more trustworthy, as shown by Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

SQL 127

SQL AWS Analytics Analytics 127

AWS Machine Learning Blog

FEBRUARY 25, 2025

Services class Texts belonging to this class consist of explicit requests for services such as room reservations, hotel bookings, dining services, cinema information, tourism-related inquiries, and similar service-oriented requests. Embeddings are vector representations of text that capture semantic and contextual information.

Algorithm 76

Algorithm Machine Learning Machine Learning K-nearest Neighbors 76

AWS Machine Learning Blog

NOVEMBER 9, 2023

This benefits enterprise software development and helps overcome the following challenges: Sparse documentation or information for internal libraries and APIs that forces developers to spend time examining previously written code to replicate usage. For more information and to get started, visit the Amazon CodeWhisperer page.

AWS 114

AWS Natural Language Processing K-nearest Neighbors Computer Science 114

Snorkel AI

APRIL 11, 2023

Even for simple tasks like information extraction, locating entities and relations can take a half an hour or more, even for simple news stories. So the key problem here is, how can we efficiently identify the most informative training examples? Annotation at word level can actually take 10 times longer than the audio clip.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

Even for simple tasks like information extraction, locating entities and relations can take a half an hour or more, even for simple news stories. So the key problem here is, how can we efficiently identify the most informative training examples? Annotation at word level can actually take 10 times longer than the audio clip.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

Even for simple tasks like information extraction, locating entities and relations can take a half an hour or more, even for simple news stories. So the key problem here is, how can we efficiently identify the most informative training examples? Annotation at word level can actually take 10 times longer than the audio clip.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52