Machine Learning

Interpretable Machine Learning for High Piezoelectric Coefficients in KNN Ceramics

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Interpretable Machine Learning for High Piezoelectric Coefficients in KNN Ceramics

A public academic platform initiated by overseas scholars Sharing information, integrating resources Exchanging academics, occasionally discussing literature High-performance piezoelectric ceramics are a crucial type of material in modern electronic devices. In pursuit of sustainable development, traditional lead-based piezoelectric materials dominated by PbZr1−xTixO3 (d33 ≈ 200-1500 pC/N) are gradually transitioning to lead-free piezoelectric ceramics based on … Read more

Introduction to nanoflann: A C++ K-Nearest Neighbors Library

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Introduction to nanoflann: A C++ K-Nearest Neighbors Library

Hello everyone! Today I want to introduce you to a super useful C++ library – nanoflann! It is an efficient library for K-nearest neighbor search, especially suitable for handling large-scale datasets. Whether you are working on machine learning, computer vision, or robotics, this library can help you quickly find the nearest neighbor points and improve … Read more

Wenzhou University Machine Learning Course Materials (6. KNN Algorithm)

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Wenzhou University Machine Learning Course Materials (6. KNN Algorithm)

Wenzhou University Machine Learning Course, Lecturer: Huang Haiguang Download link: https://github.com/fengdu78/WZU-machine-learning-course Includes PDF materials, code, etc. Continuous updates will follow. Already released: Course Videos Part One Materials (Overview) Part Two Materials (Regression) Part Three Materials (Logistic Regression) Part Four Materials (Naive Bayes) Part Five Materials (Machine Learning Practice) I will also record the course and … Read more

Gzip + KNN Outperforms BERT Classification Performance

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Gzip + KNN Outperforms BERT Classification Performance

Paper Introduction “Low-Resource” Text Classification: A Parameter-Free Classification Method with Compressors https://aclanthology.org/2023.findings-acl.426/ This paper introduces a new method for text classification, providing a non-parametric alternative to Deep Neural Networks (DNNs). Although DNNs are widely used due to their high accuracy, they require a large amount of labeled data and millions of parameters, making their computational … Read more

Understanding Ten Models: Lasso, Bayesian, KNN, Logistic, Decision Trees, Random Forests, SVM, Neural Networks, XGBoost, LightGBM

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Understanding Ten Models: Lasso, Bayesian, KNN, Logistic, Decision Trees, Random Forests, SVM, Neural Networks, XGBoost, LightGBM

Machine Learning Machine learning is an important branch of artificial intelligence, playing an increasingly significant role in areas such as data analysis, image recognition, and natural language processing in recent years. The basic concept of machine learning revolves around how to enable computers to learn and predict using data. R, as a powerful tool for … Read more

A Novel Hybrid Method for Plant Classification Based on CNN-KNN

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A Novel Hybrid Method for Plant Classification Based on CNN-KNN

A Novel Hybrid Method for Plant Classification Based on CNN-KNN Abstract: Plant classification is an interesting problem in the field of computer vision. Many researchers have completed the classification of plants through their leaves and flowers. Multiple studies have shown that leaves are the best and most consistent source of information for plant classification. However, … Read more

Breakthrough LSTM! Multivariate Data Anomaly Detection with LSTM and KNN

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Breakthrough LSTM! Multivariate Data Anomaly Detection with LSTM and KNN

Hello, I am Xiao Chu~ Today we are going to talk about a topic: using LSTM and KNN for multivariate data anomaly detection. Related Principles Multivariate Data Anomaly Detection is a very important task, especially in complex contexts such as high-dimensional data and time series data. Traditional anomaly detection methods (such as those based on … Read more

How to Build a Recommendation System Using Word2Vec

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How to Build a Recommendation System Using Word2Vec

Click the “AI Meets Machine Learning” above to select the “Star” public account Heavyweight content delivered to you first Overview Today, recommendation engines are everywhere, and people expect data scientists to know how to build one. Word2Vec is a very popular word embedding used for various NLP tasks. We will use Word2Vec to build our … Read more

Understanding Word2Vec Principles

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Understanding Word2Vec Principles

Word2Vec is an NLP tool launched by Google in 2013. Its feature is to vectorize all words, allowing for a quantitative measurement of the relationships between words and the exploration of connections among them. 01 Basics of Word Vectors Word Vector: A representation of words in a vector space. Why not use simple one-hot representation … Read more

An Overview of the Word2vec Skip-Gram Model

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An Overview of the Word2vec Skip-Gram Model

New Media Manager Author Introduction Liú Shūlóng, currently an engineer in the technology department of Daguan Data, with interests primarily in natural language processing and data mining. Word2vec is one of the achievements of the Google research team, and as a mainstream tool for obtaining distributed word vectors, it has a wide range of applications … Read more