Intelligence in manufacturing systems: the pattern recognition perspective

No abstract is available for this paper.

• Publication year

  2010

• Venue

  Control and Cybernetics

• Publication date

  Unknown publication date

• Fields of study

  Computer Science, Engineering

• Identifiers

  No identifiers available.

• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Statistical Learning Theory

  2021cited by this paper
• Learning with Kernels

  2011cited by this paper
• Building Sparse Multiple-Kernel SVM Classifiers

  2009cited by this paper
• A case-based evolutionary model for defect classification of printed circuit board images

  2008cited by this paper
• Artificial neural network models for the prediction of surface roughness in electrical discharge machining

  2008cited by this paper
• Remote sensing applications - new vistas for measurement and control

  2008cited by this paper
• An artificial neural network for fault detection in the assembly of thread-forming screws

  2008cited by this paper
• A review of the current applications of genetic algorithms in assembly line balancing

  2008cited by this paper
• Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

  2008cited by this paper
• Multiscale Isotropic Matched Filtering for Individual Tree Detection in LiDAR Images

  2007cited by this paper
• Data mining in design of products and production systems

  2007cited by this paper
• Multiclass multiple kernel learning

  2007cited by this paper
• Applying data mining to manufacturing: the nature and implications

  2007cited by this paper
• Multivariate Data Analysis

  2007influential reference
• CONCURRENT ENGINEERING : Research and Applications Dynamic Task Communication for Concurrent Processing in Distributed Systems

  2006cited by this paper
• Data Mining for Improving the Quality of Manufacturing: A Feature Set Decomposition Approach

  2006cited by this paper
• Pattern Recognition and Machine Learning

  2006cited by this paper
• Intelligent Programming of CNC Turning Operations using Genetic Algorithm

  2006cited by this paper
• Multiscale model-based feature extraction in structural texture images

  2006cited by this paper
• A neural network based methodology for machining operations selection in Computer-Aided Process Planning for rotationally symmetrical parts

  2006cited by this paper
• Implementation of the KNGR Class Library Based on the GPM and Semantic Networks for Co-design

  2005cited by this paper
• On fuzzy-rough sets approach to feature selection

  2005cited by this paper
• Neural vs. statistical classifier in conjunction with genetic algorithm based feature selection

  2005cited by this paper
• Efficient hyperkernel learning using second-order cone programming

  2004cited by this paper
• Hybrid Genetic Algorithms for Feature Selection

  2004cited by this paper
• Kernel Methods for Pattern Analysis

  2004cited by this paper
• Taming the Curse of Dimensionality in Kernels and Novelty Detection

  2004cited by this paper
• The Elements of Statistical Learning

  2003cited by this paper
• Genetic algorithm based feature selection for target detection in SAR images

  2003cited by this paper
• Feature selection with neural networks

  2002cited by this paper
• Knowledge fusion among the virtual production enterprises within the technology information infrastructure environment

  2002cited by this paper
• Fault Diagnosis of Multistage Manufacturing Processes by Using State Space Approach

  2002cited by this paper
• FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS

  2002cited by this paper
• COMPARING SPECTRAL AND OBJECT BASED APPROACHES FOR CLASSIFICATION AND TRANSPORTATION FEATURE EXTRACTION FROM HIGH RESOLUTION MULTISPECTRAL IMAGERY

  2002cited by this paper
• Greedy function approximation: A gradient boosting machine.

  2001cited by this paper
• Handbook of Process Algebra

  2001cited by this paper
• A Maximum-Likelihood Strategy for Directing Attention during Visual Search

  2001cited by this paper
• Nonlinear dimensionality reduction by locally linear embedding.

  2000cited by this paper
• A global geometric framework for nonlinear dimensionality reduction.

  2000cited by this paper
• A framework for building intelligent manufacturing systems

  1999cited by this paper
• Support Vector Machines for Pattern Classification

  1999influential reference
• Fuzzy Models and Algorithms for Pattern Recognition and Image Processing

  1999cited by this paper
• Image processing and data analysis: the multiscale approach

  1998cited by this paper
• A decision-theoretic generalization of on-line learning and an application to boosting

  1997cited by this paper
• Engineering applications of the self-organizing map

  1996cited by this paper
• No Free Lunch Theorems for Search

  1995cited by this paper
• On the semantics of theory change: arbitration between old and new information

  1993cited by this paper
• Handbook of pattern recognition & computer vision

  1993cited by this paper
• Unified Theories of Cognition

  1991cited by this paper
• Congruence, similarity, and symmetries of geometric objects

  1987cited by this paper
• Digital image processing

  1980cited by this paper
• Knowing and guessing : a quantitative study of inference and information

  1969cited by this paper
• On the mean accuracy of statistical pattern recognizers

  1968cited by this paper

• Intelligent support system for monitoring machining processes

  2022cites this paper
• Ecodesign of Technological Processes with the Use of Decision Trees Method

  2017cites this paper
• Machine-Part Grouping in Flexible Manufacturing: Formalisation and the Use of Genetic Algorithms

  2010cites this paper
• Metody sztucznej inteligencji w rozwoju systemów informatycznych dla przedsiębiorstw produkcyjnych i wodociągowych

  2009cites this paper