Understanding Supervised Learning in Machine Learning

May 15, 2024

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Supervised learning involves learning an unknown mapping through a set of input-output pairs. This type of learning can be used to classify data or predict numerical values. Factors like sample size, hypothesis set, and learning algorithm influence the learnability of the process. Probably Approximately Correct (PAC) learning and Vapnik-Chervonenkis (VC) dimension help understand the complexities involved. Noise and model complexity are also important considerations in supervised learning.

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Understanding Supervised Learning in Machine Learning

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Un Supervised Learning

Un Supervised Learning. & Self Organizing Maps. Un Supervised Competitive Learning. In Hebbian networks, all neurons can fire at the same time Competitive learning means that only a single neuron from each group fires at each time step Output units compete with one another.

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Supervised Learning

Supervised Learning. Introduction. Key idea Known target concept (predict certain attribute) Find out how other attributes can be used Algorithms Rudimentary Rules (e.g., 1R) Statistical Modeling (e.g., Na ï ve Bayes) Divide and Conquer: Decision Trees Instance-Based Learning

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Supervised learning

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Supervised Learning

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Supervised machine learning

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Introduction to Supervised Machine Learning Concepts

Introduction to Supervised Machine Learning Concepts. PRESENTED BY B. Barla Cambazoglu ⎪ February 21, 2014. Guest Lecturer’s Background. Lecture Outline. Basic concepts in supervised machine learning Use case: Sentiment-focused web crawling. Basic Concepts. What is Machine Learning?.

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Semi-Supervised Learning

Semi-Supervised Learning. Maria-Florina Balcan 02/18/2010. Supervised Learning: Formalization (PAC). X - instance space S l ={(x i , y i )} - labeled examples drawn i.i.d. from some distr. D over X and labeled by some target concept c * labels 2 {-1,1} - binary classification.

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Supervised learning vs. unsupervised learning

Unsupervised Learning Reading: Chapter 8 from Introduction to Data Mining by Tan, Steinbach, and Kumar, pp. 489-518 , 532 -544, 548-552 . Supervised learning vs. unsupervised learning. Unsupervised Classification of Optdigits. Attribute 1. Attribute 2. Attribute 3. 64-dimensional space.

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Supervised Learning

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Semi-supervised learning

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Supervised Learning

Business School Institute of Business Informatics. Supervised Learning. Uwe Lämmel. www.wi.hs-wismar.de/~laemmel U.laemmel@wi.hs-wismar.de. Neural Networks. Idea Artificial Neuron & Network Supervised Learning Unsupervised Learning Data Mining – other Techniques. Feed-Forward Networks

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Semi-Supervised Learning

Semi-Supervised Learning. D. Zhou, O Bousquet, T. Navin Lan, J. Weston, B. Schokopf Presents: Tal Babaioff. Semi Supervised Learning. Use small number of labeled data to label large amount of cheap unlabeled data. Basic idea: similar examples should be given the same classification.

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Semi-supervised Learning

Semi-supervised Learning. Rong Jin. Semi-supervised learning. Label propagation Transductive learning Co-training Active learning. Label Propagation. A toy problem Each node in the graph is an example Two examples are labeled Most examples are unlabeled

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Semi-Supervised Learning

Semi-Supervised Learning. Jing xu. Slides. From: Xiaojin (Jerry) Zhu ---An associate professor in the Department of Computer Sciences at University of Wisconsin-Madison. Outline. What’s semi-supervised learning? Self-training algorithm

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Supervised Learning

Supervised Learning. Berlin Chen 2005. References: 1. E. Alpaydin, Introduction to Machine Learning , Chapter 2 2. Tom M. Mitchell, Machine Learning , Chapter 7 3. T. Hastie et al., The Elements of Statistical Learning , Chapter 2. Supervised Learning. Learning with a Teacher

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Supervised Learning

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Supervised Learning

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Understanding-Machine-Learning-Concepts

Exploring "Machine Learning" with Takeoff Edu Group! Machine learning is like teaching computers to learn from information and decide things by themselves. It's used in different areas like healthcare, finance, and entertainment, making a big difference in our everyday lives. Join us to unravel the magic behind this innovative technology!

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