Please discuss about the algorithm or method you have applied for pattern recognition and share only your direct experience and let us know the advantages and/or disadvantages of them. Thanks
Pattern recognition is defined as the automated recognition of patterns in data.
Pattern recognition is closely related to artificial intelligence and machine learning. However, machine learning is one approach to pattern recognition and other approaches include hand-crafted rules or heuristics and there are other approaches in this regard.
Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature.
Some algorithms for pattern recognition:
- Decision trees
- Decision lists
- Kernel estimation
- K-nearest-neighbor algorithms
- Naive Bayes classifier
- Neural networks
- Perceptrons
- Support vector machines
- Gene expression programming
- Deep learning methods
- Gaussian process regression
- Linear regression and extensions
- Independent component analysis
- Principal components analysis
- Bayesian networks
- Markov random fields
- …
1-Autoencoder, Low Rank Approximation and Pseudoinverse Learning Algorithm.
2- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.
Compressive strength prediction of environmentally friendly concrete using artificial neural networks
Solid waste in the form of construction debris is one of the major environmental concerns in the world. Over 20 million tons of construction waste materials are generated in Tehran each year. A large amount of these materials can be recycled and reused as recycled aggregate concrete (RAC) for general construction, pavement and a growing number of other works that drive the demand for RAC. This paper aims to predict RAC compressive strength by using Artificial Neural Network (ANN). The training and testing data for ANN model development were prepared using 139 existing sets of data derived from 14 published literature sources. The developed ANN model uses six input features namely water cement ratio, water absorption, fine aggregate, natural coarse aggregate, recycled coarse aggregate, water-total material ratio. The ANN is modelled in MATLAB and applied to predict the compressive strength of RAC given the foregoing input features. The results indicate that the ANN is an efficient model to be used as a tool in order to predict the compressive strength of RAC which is comprised of different types and sources of recycled aggregates.
Article Compressive strength prediction of environmentally friendly ...
How do i work with multinominal data sets using Decision Tress in weka. What is the right samplying filters to apply to remove the multi nominal values
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