Unsupervised learning neural networks

Part 2 of ebook "The elements of statistical learning: Data mining, inference, and prediction (Second edition)" provides readers with contents including: Chapter 10 - Boosting and additive trees; Chapter 11 - Neural networks; Chapter 12 - Support vector machines and flexible discriminants; Chapter 13 - Prototype methods and nearest-neighbors; Chapter 14 - Unsupervised learning; Chapter 15 - Random forests; Chapter 16 - Ensemble learning; Chapter 17 - Undirected graphical models; Chapter 18 - High-dimensional problems p ≫ N;...

409p daonhiennhien 03-07-2024 4 1   Download

In this paper, we propose a framework based on very deep convolutional neural network autoencoder for image retrieval, called AIR (Autoencoders for Image Retrieval). Our proposed framework allows to learn feature vectors directly from the raw image and in an unsupervised manner.

8p visystrom 22-11-2023 6 3   Download

This course is an introduction to the subject of Artificial Neural Networks and Genetic Algorithms, two very new subjects forming part of Distributed Artificial Intelligence. As you leaf through these notes you will notice that they are full of mathematical equations. The reason is simple: these subjects are inherently mathematical. However the course and assessments are such that it will be possible for you to pass if you do not touch the equations. However if you wish to gain a good pass you must attempt to master the equations.

136p haojiubujain08 01-11-2023 5 2   Download

Why this book on computational intelligence? Need arose from a graduate course, where students do not have a deep background of artificial intelligence and mathematics. Therefore the introductory nature, both in terms of the CI paradigms and mathematical depth. While the material is introductory in nature, it does not shy away from details, and does present the mathematical foundations to the interested reader.

311p haojiubujain07 20-09-2023 6 3   Download

Ebook "Machine learning and its applications" includes content: Comparing machine learning and knowledge discovery in databases -- an application to knowledge discovery in texts, unsupervised learning of probabilistic concept hierarchies, function decomposition in machine learning, case based reasoning,...and other contents.

334p haojiubujain07 20-09-2023 6 3   Download

Ebook "Introduction to machine learning" includes content: Preliminaries, boolean functions, using version spaces for learning, neural networks, statistical learning, decision trees, inductive logic programming, computational learning theory, unsupervised learning,... and other contents.

209p haojiubujain07 20-09-2023 6 2   Download

Unsupervised compression algorithms applied to gene expression data extract latent or hidden signals representing technical and biological sources of variation. However, these algorithms require a user to select a biologically appropriate latent space dimensionality.

27p viarchimedes 26-01-2022 7 0   Download

A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program.

11p vioklahoma2711 19-11-2020 12 2   Download

The techniques of supervised ones are applied to the data domain in order to have a comparison between the evaluated system of POSSUM and the advantage of Neural network. The comparisons are based on the rate of mortality and morbidity of patients. The outcome set of unsupervised learning techniques is compared to the results of supervised ones.

8p tamynhan8 04-11-2020 19 1   Download

Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are built with only local context and one representation per word. This is problematic because words are often polysemous and global context can also provide useful information for learning word meanings.

10p nghetay_1 07-04-2013 57 1   Download

The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the sense that they use a neighborhood function to preserve the topological properties of the input space and they have been used to create an ordered representation of multi-dimensional data which simplifies complexity and reveals meaningful relationships. Prof. T. Kohonen in the early 1980s first established the relevant theory and explored possible applications of SOMs. ...

0p bi_bi1 13-07-2012 107 18   Download

Nội dung môn học gồm lý thuyết và thực hành: 1. Lý thuyết đưa ra các chủ đề về neural network và ứng dụng vào quá trình học có/không có giám sát (supervised/unsupervised learning) 2. Thực hành với Matlab và ứng dụng của thuật toán học trong neural network. 3. Các ứng dụng của Neural Networks trong lĩnh vực điện tử-viễn thông.

40p haiph37 15-09-2010 167 56   Download