supervised learning

This paper introduces concept of data mining, and presents two methods of data mining: supervised learning and unsupervised learning.

However, the result of the feature selection in unsupervised learning is not as satisfactory as that in supervised learning.

Semi-supervised learning algorithms, which consider both labeled and unlabeled data, can improve learning effectiveness significantly.

The comparison and analysis of geometric information's impact on semi-supervised learning is performed through the experiments.

Furthermore, presented a new framework exploiting active learning and semi-supervised learning to attack the disadvantage. . .

Semi-supervised learning - Combines both labeled and unlabeled examples to generate an appropriate function or classifier.

This paper reviews the problem of semi-supervised learning the basic idea of the status quo, Summarized the current study difficult.

The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory.

We apply a regularized diffusion framework to solve the supervised learning image segmentation problem.

The machine learning accuracy can be improved by making use of both labeled and unlabeled data in semi-supervised learning.

for a balanced training set, it proposes an entropy-based semi-supervised learning algorithm. 3.

A model of supervised learning is proposed based on the mechanism of immune recognition and applied into the Human Face Recognition.

In this paper, we discuss the problem of using semi-supervised learning method to do video semantic annotation.

Supervised learning is the most common technique for training neural networks and decision trees.

The control system works at distal supervised learning control mode or extreme control mode.

Supervised learning with the use of regression and classification networks with sparse data sets will be explored.

However, traditional supervised learning techniques typically require a large number of labeled examples to learn an accurate classifier.

The author uses a instance about heart disease and explain the process of supervised learning.

as we all know , the methods of feature selection for supervised learning perform pretty well with strong practice and simple operation.

Supervised learning based data mining technology with its application to heart diseases risk detection

Construction of Multi-classification Decision Tree Combining Unsupervised and Supervised Learning Strategy

On the Global Optimal Theory of the Outer-Supervised Learning Feedforward Neural Networks

Comparison Between Supervised Learning and Unsupervised Learning in Neural Networks

Supervised Learning of Heuristic Function for Refutation

A New Supervised Learning Algorithm for Probabilistic Neural Network

A Hybrid Supervised Learning Control of Continuous Stirred Tank Reactor

Dynamic infrared imagery analysis method based on knowledge representation and supervised learning

A Fast Supervised Learning Algorithm of Multilayer Feedforward Network Based on Improved Conjugate Gradient Method

Algorithm for Semi-Supervised Learning in Image Retrieval

Behavior Modeling and Abnormality Detection Based on Semi-Supervised Learning Method

Benchmark Data Sets for Semi-Supervised Learning

A Model of Supervised Learning Based on Artificial Immune and the Application

A Method of Choosing Web Services based on Supervised Learning Principle

On-line IDS Pattern Analysis by ESOM Based on Supervised Learning