How artificial neural network works in machine learning?
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|---|---|
| Artificial intelligence, specifically the process termed “machine learning” and “neural networking,” involves complex algorithms that allow computers to improve the decision-making process based on repeated input of empirical data (e. g., databases and ECGs). | |
| This paper suggests a machine learning approach in which a neural network automatically determines the data by learning from examples. | |
36 Citations | Although some data analysis techniques are described as “learning,”10 machine learning involves “programming a digital computer to behave in a way which, if done by human beings or animals, would be described as involving the process of learning.”11 As practical matter, all useful artificial intelligence is built on machine learning, and nearly all machine learning is built on neural networks. |
20 Citations | The artificial neural network provides an advanced computing model, which allows more factors to be involved. |
10 Citations | Artificial Neural Network has the ability to learn from previous data. |
01 Sep 2008 20 Citations | The artificial neural network has the ability to generalize and can interpolate in between the training data. |
08 May 1989 26 Citations | One popular artificial neural network model, the back-propagation algorithm, promises to be a powerful and flexible learning model. |
| Artificial Neural Networks have proven to be a very powerful machine learning algorithm which can be adequate to learn successfully a variety of tasks. | |
| Especially suitable for students and researchers in computer science, engineering, and psychology, this text and reference provides a systematic development of neural network learning algorithms from a computational perspective, coupled with an extensive exploration of neural network expert systems which shows how the power of neural network learning can be harnessed to generate expert systems automatically. | |
Open access 01 Mar 1987 71 Citations | The proposed neuronal model and learning mechanism offer a new building block for constructing neural network‐like computer arthitectures for artificial intelligence. |
Related Questions
What is Artificial neural networks?4 answersArtificial Neural Networks (ANNs) are computational models inspired by the human brain's information processing system. ANNs consist of interconnected processing elements called neurons, which work together to solve problems. Similar to the brain, ANNs learn from examples and adjust their synaptic connections to improve performance. ANNs have various applications, including pattern recognition and data classification. They aim to mimic the behavior of the brain by implementing simplified models of neurons and their interconnections. ANNs can receive stimuli, emit signals, and communicate with other neurons, enabling learning and problem-solving. ANNs are a type of machine learning that uses the brain's processing as a basis for developing algorithms to model complex patterns and prediction problems. They are also a part of deep learning, which processes data in complex patterns and automates tasks that require human-like intelligence.
What is Artificial Neural Network?5 answersAn Artificial Neural Network (ANN) is an information processing paradigm inspired by the way biological nervous systems, such as the brain, process data. It consists of interconnected processing elements called neurons that work together to solve problems. ANNs learn from examples and are trained for specific applications like pattern recognition or data classification. ANNs aim to mimic the behavior of the human brain by implementing a simplified model. The brain learns because neurons can communicate with each other through synapses. ANNs are computational methods that belong to the field of Machine Learning and try to solve problems by learning tasks. They are widely used for regression and classification tasks and can be trained using various algorithms and techniques. Deep learning models, a type of neural network, are also discussed in relation to ANNs.
How does machine learning work?5 answersMachine learning is a branch of artificial intelligence that enables computers to learn from data and make decisions without explicit programming. It works by using data to identify patterns, make decisions, and learn from them. Instead of relying on predetermined equations, machine learning algorithms learn directly from data using computational techniques. The learning process involves experimenting with different rules and learning what works and what doesn't. Machine learning techniques include supervised, unsupervised, and reinforcement learning. Deep learning, a subset of machine learning, involves training multilayer artificial neural networks with minimal data. Machine learning has various applications in fields such as healthcare, education, and problem-solving. It has the potential to revolutionize these fields by extracting relevant data, improving decision-making, and fostering personalized learning.
What is AI neural network and what is it used for?1 answersAI neural network is a subset of artificial intelligence that involves the use of biologically inspired models for information processing. It is not an exact replica of how the brain functions, but it has shown promising results in forecasting and business classification applications. Neural networks learn by updating their architecture and connection weights, allowing them to efficiently perform tasks. They can learn from available training patterns or automatically learn from examples or input-output relations. Neural networks have been used in various fields such as healthcare, where they have been employed for greater accuracy and instant results. They have also been used in game AI, specifically in the fighting game genre, to select actions based on the game state, providing a challenging and satisfying experience for players. Additionally, neural networks have been utilized in the design and evaluation of AI systems, generating and analyzing neural networks based on user interactions and articulating their behavior in natural language.
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