Flipboard

MAY 25, 2025

XGBoost achieved the highest accuracy (0.99) and perfect detection (1.00) of normal traffic and signal drift, outperforming Random Forest (0.98), Support Vector Machine (0.97), Logistic Regression (0.93), and K Nearest Neighbors (0.81). These results highlight XGBoost as a reliable solution for wireless network security.

K-nearest Neighbors 73

K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 73

Towards AI

APRIL 4, 2024

According to IBM, machine learning is a subfield of computer science and artificial intelligence (AI) that focuses on using data and algorithms to simulate human learning processes while progressively increasing their accuracy.

K-nearest Neighbors 80

K-nearest Neighbors Machine Learning Machine Learning Decision Trees 80

Flipboard

JANUARY 19, 2025

Leveraging a comprehensive dataset of diverse fault scenarios, various machine learning algorithms—including Random Forest (RF), K-Nearest Neighbors (KNN), and Long Short-Term Memory (LSTM) networks—are evaluated. An ensemble methodology, RF-LSTM Tuned KNN, is proposed to enhance detection accuracy and robustness.

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K-nearest Neighbors Machine Learning Machine Learning Algorithm 65

Flipboard

JANUARY 2, 2025

The proven classifier models, k - nearest neighbor (KNN) and support vecter machine (SVM) models, are integrated to classify the extracted deep CNN features. 3 distinct experiments with the same deep CNN features but different classifier models (softmax, KNN, SVM) are performed.

K-nearest Neighbors 73

K-nearest Neighbors Cross Validation Computer Science Computer Science 73

IBM Journey to AI blog

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

AWS Machine Learning Blog

NOVEMBER 9, 2023

Formally, often k-nearest neighbors (KNN) or approximate nearest neighbor (ANN) search is often used to find other snippets with similar semantics. He received his PhD in Computer Science from Purdue University in 2008. in Computer Science from University of Massachusetts Amherst in 2006.

AWS 123

AWS Natural Language Processing K-nearest Neighbors Computer Science 123

PyImageSearch

JUNE 19, 2023

This technique expresses a text item as a feature vector, which can be used to compute cosine similarity with other item feature vectors. Figure 7: TF-IDF calculation (source: Towards Data Science ). The item ratings of these -closest neighbors are then used to recommend items to the given user. That’s not the case.

K-nearest Neighbors 57

K-nearest Neighbors Clustering Algorithm Deep Learning 57

Flipboard

MAY 5, 2025

Vector and Semantic Search: Leverages machine learning-powered search techniques, including k-NN (k-nearest neighbors) and dense vector embeddings, for applications like AI-driven search, recommendation systems, and similarity search. Or requires a degree in computer science? Thats not the case.

AWS 119

AWS Clustering Deep Learning Deep Learning 119

AWS Machine Learning Blog

JUNE 9, 2023

To solve the problem of finding the field of study for any given paper, simply perform a k-nearest neighbor search on the embeddings. Da got his PhD in computer science from the Johns Hopkins University. In this case, the model reaches an MRR of 0.31 on the test set of the constructed graph.

ML 108

ML ML K-nearest Neighbors Machine Learning 108

PyImageSearch

MAY 12, 2025

Powering Neural Search : Enables advanced similarity-based retrieval using OpenSearchs k-NN (k-Nearest Neighbors) indexing. By defining an index mapping correctly, OpenSearch can efficiently store and retrieve movie data while leveraging k-NN (k-Nearest Neighbors) search to find similar movies based on embeddings.

AWS 73

AWS K-nearest Neighbors Deep Learning Deep Learning 73

AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of treating each input as entirely unique, we can use a distance-based approach like k-nearest neighbors (k-NN) to assign a class based on the most similar examples surrounding the input. To make this work, we need to transform the textual interactions into a format that allows algebraic operations.

Algorithm 85

Algorithm Machine Learning Machine Learning K-nearest Neighbors 85

AWS Machine Learning Blog

FEBRUARY 10, 2023

We design a K-Nearest Neighbors (KNN) classifier to automatically identify these plays and send them for expert review. He has a degree in Mathematics and Computer Science from the University of Illinois at Urbana Champaign. Some plays are mixed into other coverage types, as shown in the following figure (right).

ML 94

ML ML Machine Learning Machine Learning 94

Flipboard

FEBRUARY 7, 2025

In most cases, you will use an OpenSearch Service vector database as a knowledge base, performing a k-nearest neighbor (k-NN) search to incorporate semantic information in the retrieval with vector embeddings. Jon holds a Bachelor of the Arts from the University of Pennsylvania, and a Master of Science and a Ph.

Database 86

Database AWS Python ML 86

Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Computer Science Computer Science 52

Snorkel AI

JANUARY 26, 2023

I am a PhD student in the computer science department at Stanford, advised by Chris Ré working on some broad themes of understanding data-centric AI, weak supervision and theoretical machine learning. So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Computer Science Computer Science 52

Mlearning.ai

MAY 23, 2023

Read the full article here —  [link] For final-year students pursuing a degree in computer science or related disciplines, engaging in machine learning projects can be an excellent way to consolidate theoretical knowledge, gain practical experience, and showcase their skills to potential employers.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Python 52

Snorkel AI

APRIL 28, 2023

Ce Zhang is an associate professor in Computer Science at ETH Zürich. You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier. This talk was followed by an audience Q&A conducted by Snorkel AI’s Priyal Aggarwal.

ML 52

ML ML Machine Learning Machine Learning 52

Snorkel AI

APRIL 28, 2023

Ce Zhang is an associate professor in Computer Science at ETH Zürich. You can approximate your machine learning training components into some simpler classifiers—for example, a k-nearest neighbors classifier. This talk was followed by an audience Q&A conducted by Snorkel AI’s Priyal Aggarwal.

ML 52

ML ML Machine Learning Machine Learning 52

Pickl AI

SEPTEMBER 12, 2024

Artificial Intelligence (AI): A branch of computer science focused on creating systems that can perform tasks typically requiring human intelligence. K K-Means Clustering: An unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

PyImageSearch

SEPTEMBER 16, 2024

For example, The K-Nearest Neighbors algorithm can identify unusual login attempts based on the distance to typical login patterns. The Local Outlier Factor (LOF) algorithm measures the local density deviation of a data point with respect to its neighbors. Or requires a degree in computer science?

Clustering 57

Clustering Algorithm Machine Learning Machine Learning 57