Approximation of reachable sets using optimal control and support vector machines

Rasmussen, Martin; Rieger, Janosch; Webster, Kevin

doi:10.1016/j.cam.2016.06.015

Cited by 16 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One simple form this method can take is to store all experienced items in a representational space with a similarity gradient around each for use in future prediction or classification. This is exemplified in recent kernel methods, which use a variety of similarity metrics and often remove redundant stored items to provide a more efficient representation [1]; for example, support vector machines use kernel functions to draw decision boundaries between categories [2], often resulting in highly accurate classification performance [3][4][5].…”

Section: Spatial Methodsmentioning

confidence: 99%

What does the mind learn? A comparison of human and machine learning representations

Spicer

Sanborn

2019

Current Opinion in Neurobiology

View full text Add to dashboard Cite

show abstract

Section: Spatial Methodsmentioning

confidence: 99%

What does the mind learn? A comparison of human and machine learning representations

Spicer

Sanborn

2019

Current Opinion in Neurobiology

View full text Add to dashboard Cite

show abstract

“…Semantic matching extracts the corresponding semantic features and then performs semantic retrieval of user advertisements [18]. The semantic features of users and advertisements are extracted separately to calculate relevance.…”

Section: Related Workmentioning

confidence: 99%

“…5 and 6 show the scatter plot of iris measurements and the iris classification regions, respectively. Thus, it selects k nearest neighbors of x in the classification and places x into the class to which most of k neighbors belong [17], [18].…”

Section: Training Of Svm Classifiermentioning

confidence: 99%

A SVM Multi-Class Image Classification Method Based on DE and KNN in Smart City Management

Shu

Cai

2019

IEEE Access

View full text Add to dashboard Cite

When directly operated in an image, good results are always difficult to achieve via conventional methods because they have poor high-dimensional performance. Support vector machine (SVM) is a type of machine learning method with solid foundation that is developed based on traditional statistics. It is also a theory for statistical estimation and predictive learning of objects. This paper optimizes the structure of SVM classification tree with differential evolution (DE) and designs the corresponding DE algorithm to effectively solve the problem of image classification of complex background cases in smart city management systems. In the training process of SVM classification tree, it obtains an optimal two-class classification scheme in every node by means of DE, initially separates the classes that are easy to be separated and then the less easy ones, and finally adaptively generates the best classification tree. The simulation experiment proves that the proposed algorithm is effective when applied to smart city management systems.

show abstract

“…, ( , ) with input ∈ ( -dimensional input space) and class labels (target output) ∈ {1, −1}. An SVM is employed to implement the classification [10]. This SVM constructs a hyperplane as a decision surface to maximize the margin of separation between positive and negative examples.…”

Section: Efd-based Algorithm and Svmmentioning

confidence: 99%

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Lin

2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine-(SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multiple sensing devices when concurrently detecting different patterns. Therefore, to resolve this issue, this study proposes a novel framework for establishing indicators and corresponding thresholds to identify patterns in the system; it employs a feature-based scheme that integrates principal component analysis (PCA) with an SVM for effectively detecting patterns in the system. Experiments were conducted using a tactile and optical measurement system. The experimental results demonstrated that the proposed method can effectively identify patterns in multisensor systems by using a feature-based algorithm that combines PCA and SVM classification for detecting various patterns. Moreover, the proposed framework established alarm indicators and corresponding thresholds that can be used for pattern detection.

show abstract

Approximation of reachable sets using optimal control and support vector machines

Cited by 16 publications

References 27 publications

What does the mind learn? A comparison of human and machine learning representations

What does the mind learn? A comparison of human and machine learning representations

A SVM Multi-Class Image Classification Method Based on DE and KNN in Smart City Management

PCA/SVM-Based Method for Pattern Detection in a Multisensor System

Contact Info

Product

Resources

About