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Hybrid Kohonen SOM
In artificial neural networks, a hybrid Kohonen self-organizing map is a type of self-organizing map (SOM) named for the Finland, Finnish professor Teuvo Kohonen, where the network architecture consists of an input layer fully connected to a 2–D SOM or Kohonen layer. The output from the Kohonen layer, which is the winning neuron, feeds into a hidden layer and finally into an output layer. In other words, the Kohonen SOM is the front–end, while the hidden and output layer of a multilayer perceptron is the back–end of the hybrid Kohonen SOM. The hybrid Kohonen SOM was first applied to machine vision systems for image classification and image recognition, recognition. Hybrid Kohonen SOM has been used in weather prediction and especially in forecasting stock prices, which has made a challenging task considerably easier. It is fast and efficient with less classification error, hence is a better predictor, when compared to Kohonen SOM and backpropagation networks.Mark O. Afolab ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Artificial Neural Network
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called ''edges''. Neurons and edges typically have a ''weight'' that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Self-organizing Map
A self-organizing map (SOM) or self-organizing feature map (SOFM) is an unsupervised machine learning technique used to produce a low-dimensional (typically two-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data. For example, a data set with p variables measured in n observations could be represented as clusters of observations with similar values for the variables. These clusters then could be visualized as a two-dimensional "map" such that observations in proximal clusters have more similar values than observations in distal clusters. This can make high-dimensional data easier to visualize and analyze. An SOM is a type of artificial neural network but is trained using competitive learning rather than the error-correction learning (e.g., backpropagation with gradient descent) used by other artificial neural networks. The SOM was introduced by the Finnish professor Teuvo Kohonen in the 1980s and therefore is sometim ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Finland
Finland ( fi, Suomi ; sv, Finland ), officially the Republic of Finland (; ), is a Nordic country in Northern Europe. It shares land borders with Sweden to the northwest, Norway to the north, and Russia to the east, with the Gulf of Bothnia to the west and the Gulf of Finland across Estonia to the south. Finland covers an area of with a population of 5.6 million. Helsinki is the capital and largest city, forming a larger metropolitan area with the neighbouring cities of Espoo, Kauniainen, and Vantaa. The vast majority of the population are ethnic Finns. Finnish, alongside Swedish, are the official languages. Swedish is the native language of 5.2% of the population. Finland's climate varies from humid continental in the south to the boreal in the north. The land cover is primarily a boreal forest biome, with more than 180,000 recorded lakes. Finland was first inhabited around 9000 BC after the Last Glacial Period. The Stone Age introduced several differ ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Teuvo Kohonen
Teuvo Kalevi Kohonen (11 July 1934 – 13 December 2021) was a prominent Finnish academic ( Dr. Eng.) and researcher. He was professor emeritus of the Academy of Finland.Teuvo Kohonen. Dr. Eng., Emeritus Professor of the Academy of Finland; Academician a biography from his university, last accessed on 27 January 2022. Career Kohonen studied at the and graduated with a master's degree in engineering in 1957. He received his doctorate in 1962 and stayed at the university with a faculty position until 1993. He was an accademy professor of the[...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Multilayer Perceptron
A multilayer perceptron (MLP) is a fully connected class of feedforward artificial neural network (ANN). The term MLP is used ambiguously, sometimes loosely to mean ''any'' feedforward ANN, sometimes strictly to refer to networks composed of multiple layers of perceptrons (with threshold activation); see . Multilayer perceptrons are sometimes colloquially referred to as "vanilla" neural networks, especially when they have a single hidden layer. An MLP consists of at least three layers of nodes: an input layer, a hidden layer and an output layer. Except for the input nodes, each node is a neuron that uses a nonlinear activation function. MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable.Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function '' Mathematics of Control, Signals, and Systems'' ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Vision
Machine vision (MV) is the technology and methods used to provide imaging-based automatic inspection and analysis for such applications as automatic inspection, process control, and robot guidance, usually in industry. Machine vision refers to many technologies, software and hardware products, integrated systems, actions, methods and expertise. Machine vision as a systems engineering discipline can be considered distinct from computer vision, a form of computer science. It attempts to integrate existing technologies in new ways and apply them to solve real world problems. The term is the prevalent one for these functions in industrial automation environments but is also used for these functions in other environment vehicle guidance. The overall machine vision process includes planning the details of the requirements and project, and then creating a solution. During run-time, the process starts with imaging, followed by automated analysis of the image and extraction of the requir ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Image Classification
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Image Recognition
Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. The scientific discipline of computer vision is concerned with the theory ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Backpropagation
In machine learning, backpropagation (backprop, BP) is a widely used algorithm for training feedforward neural network, feedforward artificial neural networks. Generalizations of backpropagation exist for other artificial neural networks (ANNs), and for functions generally. These classes of algorithms are all referred to generically as "backpropagation". In Artificial neural network#Learning, fitting a neural network, backpropagation computes the gradient of the loss function with respect to the Glossary of graph theory terms#weight, weights of the network for a single input–output example, and does so Algorithmic efficiency, efficiently, unlike a naive direct computation of the gradient with respect to each weight individually. This efficiency makes it feasible to use gradient methods for training multilayer networks, updating weights to minimize loss; gradient descent, or variants such as stochastic gradient descent, are commonly used. The backpropagation algorithm works by ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
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