Feature Learning Lecture Notes and Tutorials PDF Download

In machine learning, feature learning or representation learning is a set of techniques that learn a feature: a transformation of raw data input to a representation that can be effectively exploited in machine learning tasks. This obviates manual feature engineering, which is otherwise necessary, and allows a machine to both learn at a specific task (using the features) and learn the features themselves: to learn how to learn. Feature learning can be divided into two categories: supervised and unsupervised feature learning, analogous to these categories in machine learning generally.

Feature Learning Lecture Notes and Tutorials PDF

Learning with Whom to Share in Multi-task Feature Learning

Learning with Whom to Share in Multi-task Feature Learning

the standard MTL paradigm where all tasks are in a ... Introduction. Multi-task learning (MTL) is a learning paradigm ... ference on Machine Learning, Bellevue, WA, USA, 2011. ... always improve the baseline approach (i.e., where all tasks are ...by Z Kang · ‎2011 · ‎Cited by 316 · ‎Related articles
Download
visual feature learning

visual feature learning

Learning is incremental and makes only weak assumptions about the task environment. I begin by introducing an infinite feature space that contains ...by JH PIATER · ‎2001 · ‎Cited by 44 · ‎Related articles
Download
Multiview Feature Learning

Multiview Feature Learning

You can see objects even when images contain no features. Roland Memisevic (Frankfurt, Montreal). Multiview Feature Learning. Tutorial at IPAM 2012. 19 / 163 ...
Download
Feature Selection for Machine Learning

Feature Selection for Machine Learning

Feature selection is often an essential data processing step prior to applying a ... wrapper feature se- lector that uses a specific learning algorithm to guide ...
Download
Sub2Vec: Feature Learning for Subgraphs

Sub2Vec: Feature Learning for Subgraphs

In this context, learning discriminative feature representation of subgraphs can help in leveraging existing machine learning algorithms more widely on graph ...by B Adhikari · ‎Cited by 27 · ‎Related articles
Download
Causal feature learning: an overview

Causal feature learning: an overview

by K Chalupka · 2017 · Cited by 16 — 1 Introduction. Causal feature learning (CFL) is an unsupervised machine learning and causal inference framework with two goals: (1) the formation of high-level ...
Download
Multi-Task Feature Learning

Multi-Task Feature Learning

Our algorithm can also be used, as a special case, to simply select – not learn – a few common features across the tasks. 1 Introduction. Learning multiple related ...by A Argyriou · ‎Cited by 1418 · ‎Related articles
Download
Machine Learning Feature Creation and Selection

Machine Learning Feature Creation and Selection

Feature selection occurs naturally as part of the machine learning algorithm. ◇ example: L1-regularized linear regression. Jeff Howbert. Introduction to Machine ...
Download
Machine Learning Feature Creation and Selection

Machine Learning Feature Creation and Selection

Jeff Howbert. Introduction to Machine Learning. Winter 2014. 2. ○ Well-conceived new features can sometimes capture the important information in a dataset.
Download
Convex Multi-Task Feature Learning

Convex Multi-Task Feature Learning

method can both improve the performance relative to learning each task in- dependently and lead to ... multi-task generalization of the 1-norm regularization known to provide sparse variable ... prototype task. From the table we see that our MTL-FEAT algorithm improves ... A machine learning approach to conjoint analysis.by A Argyriou · ‎Cited by 1440 · ‎Related articles
Download
Unsupervised Feature Learning in Computer Vision

Unsupervised Feature Learning in Computer Vision

establish a connection between slow-feature learning and metric learning, and ... To this end, we introduce a new architecture and loss for training deep fea-.by R Goroshin · ‎2015 · ‎Cited by 2 · ‎Related articles
Download
Context Encoders: Feature Learning by Inpainting

Context Encoders: Feature Learning by Inpainting

Abstract. We present an unsupervised visual feature learning algo- rithm driven by ... tributes and use the coherence of tracked patches to guide the training [39].by D Pathak · ‎Cited by 2282 · ‎Related articles
Download
Convex Multi-Task Feature Learning

Convex Multi-Task Feature Learning

2 Learning Sparse Multi-Task Representations. In this section, we present our formulation for multi-task feature learn- ing. We begin by introducing our notation.by A Argyriou · ‎Cited by 1448 · ‎Related articles
Download
Model and feature selection 10601 Machine Learning

Model and feature selection 10601 Machine Learning

Logistic regression: Selecting features (basis functions). – Decision trees: ... Simple greedy model selection from earlier in the lecture ... Open book, open notes.
Download
Feature Subset Selection Bias for Classification Learning

Feature Subset Selection Bias for Classification Learning

research in machine learning and data mining (Guyon ... bias known as 'feature subset selection bias' or 'selec- tion bias' ... Feature selection bias can also ad-.by SK Singhi · ‎Cited by 104 · ‎Related articles
Download
Feature Hashing for Large Scale Multitask Learning

Feature Hashing for Large Scale Multitask Learning

Feature Hashing for Large Scale Multitask Learning. Josh Attenberg ... Eq. (1) is often famously referred to as the kernel-trick. It ... on Machine Learning (ICML).by J Attenberg · ‎Cited by 9 · ‎Related articles
Download
Sparse Feature Learning for Deep Belief Networks

Sparse Feature Learning for Deep Belief Networks

Sparse Feature Learning for Deep Belief Networks. Marc'Aurelio Ranzato1 ... Abstract. Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn ... A tutorial on energy-based learning. In. G. Bakir and al.., ...by MA Ranzato · ‎Cited by 875 · ‎Related articles
Download
Feature Selection Using Principal Feature Analysis

Feature Selection Using Principal Feature Analysis

Dimensionality reduction of a feature set is a common preprocessing step used for pattern recognition and classification applications and in compression ...by ICQTX Sean · ‎Cited by 2 · ‎Related articles
Download
What is feature selection?

What is feature selection?

Oct 1, 2009 — ... engineering). – Part II: Automatic feature selection ... Learn one weight vector for each class: ... Side note: also possible to learn C efficiently ...
Download
Feature Selection for SVMs

Feature Selection for SVMs

We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds.by J Weston · ‎Cited by 1359 · ‎Related articles
Download
sequences and feature vectors

sequences and feature vectors

Standard classifiers take as input a feature vector and output its predicted label. It is possible to formulate tutorial dialogue classification problems in this way.by JP GONZÁLEZ-BRENES · ‎Cited by 7 · ‎Related articles
Download
Feature Selection Algorithms

Feature Selection Algorithms

Feature Selection Algorithms: A Survey and Experimental Evaluation ... Note this means that the sample size will depend linearly on the total number of features.
Download
Feature Driven Development

Feature Driven Development

Guide: Jennifer Schiller. Chair of Applied ... FDD decomposes the entire problem domain into tiny problems, which can be solved in a small period of time ...by S Goyal · ‎Cited by 27 · ‎Related articles
Download
Feature Selection for Classification

Feature Selection for Classification

It identifies four steps of a typical feature selection method, and categorizes the ... Figure 9 shows a summary of the feature selection methods based on the 3 ...
Download
Chapter 7 Feature Selection

Chapter 7 Feature Selection

Feature selection is not used in the system classification experiments, which will ... Two notes about the procedure in Figure 7-1: First, the choice of 70/30 split for ...
Download