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Artificial Neural Networks have proven to be a very powerful machine learning algorithm which can be adequate to learn successfully a variety of tasks.
Neural network is an effective machine learning technique for classification and regression.
Artificial neural network is proved to be an effective algorithm for dealing with recognition, regression and classification tasks.
Proceedings ArticleDOI
T. Baker, Dan Hammerstrom 
08 May 1989
26 Citations
One popular artificial neural network model, the back-propagation algorithm, promises to be a powerful and flexible learning model.
Hence, this indicates that Artificial Immune System could be implemented as an alternative learning algorithm for an Artificial Neural Network.
At the same time, artificial neural network is a machine learning system, and it will get better results with the progress of training.
Journal ArticleDOI
Xiping Zhang, Jiirgen Bode, Shouju Ren 
01 Dec 1996
43 Citations
This paper suggests a machine learning approach in which a neural network automatically determines the data by learning from examples.
Proceedings ArticleDOI
29 Mar 2018
209 Citations
The machine learning algorithm neural networks has proven to be the most accurate and reliable algorithm and hence used in the proposed system.
Open accessJournal ArticleDOI
Pedro L. Gambús, Steven L. Shafer 
01 Nov 2017-Anesthesiology
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.
Based on the results obtained, it shows that the proposed AIS learning algorithm is capable to provide a comparable forecast to that of Artificial Neural Network with Back Propagation (BP) as the learning algorithm.

Related Questions

Which one can be called an algorithm?5 answersAn algorithm can be defined as a finite sequence of instructions or rules for solving a problem within a finite amount of time. Algorithms can be applied not only in numerical contexts but also in various problem-solving areas like clinical decision-making. In the realm of digital communication, social media platforms utilize algorithms to rank content and drive user experiences through Machine Learning techniques. Moreover, the development of mathematical skills and problem-solving abilities in students can be greatly enhanced by incorporating algorithms in teaching practices. Overall, algorithms play a crucial role in guiding processes, making decisions, and solving problems efficiently across different domains, from mathematics to digital platforms and beyond.
How many types of algorithms are there in machine learning?5 answersThere are several types of algorithms in machine learning. Machine learning algorithms can be categorized into supervised learning and unsupervised learning algorithms. Supervised learning algorithms include Support Vector Machine (SVM), Logistic Regression (LR), and Decision Tree (DT). Unsupervised learning algorithms include K-Mean Cluster and Gradient Boosting. Additionally, there are other commonly used algorithms such as Nave Bayes (NB), K-Nearest Neighbors (KNN), and Random Forest (RF). Overall, there are a variety of algorithms available in machine learning, each with its own strengths and weaknesses, and the choice of algorithm depends on the specific problem being addressed.
What is the key characteristics of machine learning algorithms?5 answersMachine learning algorithms have several key characteristics. They automatically improve through experience by learning from data. These algorithms can predict outcomes without being explicitly programmed, making them based on AI and capable of revolutionizing various industries. Machine learning algorithms can handle different types of input data, such as images, texts, and numbers, and extract logical patterns from them. They also have the ability to handle big data, utilize large computing power, and make intelligent applications possible. Additionally, machine learning algorithms can be categorized into supervised, unsupervised, semi-supervised, and reinforcement learning domains, each with its own implementation and characteristics. Overall, the key characteristics of machine learning algorithms include their ability to learn from data, make predictions, handle various types of input data, and improve through experience.
What is an algorithm?5 answersAn algorithm is a sequence of instructions that solves a problem by transforming input into output in a finite amount of time. It is a computational procedure that takes input values and produces output values. Algorithms can be viewed as tools for solving well-specified computational problems, where the problem statement defines the desired input/output relationship and the algorithm describes the specific procedure for achieving that relationship. Algorithms involve a finite and unambiguous sequence of instructions or steps, which may include repetition or reiteration of operations. They can be categorized into different paradigms, such as brute force, divide and conquer, backtracking, dynamic programming, probabilistic, and greedy algorithms. The efficiency of algorithms can be measured using big-Oh, big-Omega, and big-Theta estimates, and various sorting and search algorithms are commonly used.
What is the difference between artificial intelligence, machine learning and algorithms?5 answersArtificial intelligence (AI) is a field that focuses on building machines that can think and act intelligently. It aims to mimic human intelligence and accomplish complex tasks. Machine learning (ML) is a subfield of AI that involves training algorithms to improve their performance through experience. ML algorithms can learn from data without being explicitly programmed. Algorithms, on the other hand, are sets of instructions that solve specific problems or perform specific tasks. They can be used in both AI and ML systems to process data and make decisions. In summary, AI is the broader concept of creating intelligent machines, ML is a subset of AI that focuses on algorithms that learn from data, and algorithms are the instructions used in AI and ML systems.
Can u tell me the novelity of machine learning algorithms?5 answersMachine learning algorithms have shown great potential for improving performance and efficiency in various applications. The novelity of these algorithms lies in their ability to capture interesting characteristics from data and learn from them. For example, Farhangfarpresents the OPTN BDT algorithm, which produces a near optimal fixed-depth decision tree under the Naive Bayes assumption, improving computational costs significantly while maintaining high accuracy. Mendler-Dünnerfocuses on incorporating system characteristics into algorithm design, resulting in improved performance and efficiency by utilizing individual hardware resources. Shenproposes novel methods for multi-label learning and multi-instance multi-label learning, addressing open problems in these research areas. Greenwald and Oertelhighlight the importance of feature selection, representation schemes, transfer learning, continuous learning, and learning in time-varying contexts for advancing machine learning capabilities. Tran et al.compare the robustness of simple classical models with complex deep learning techniques, providing insights into the robustness of machine learning systems.