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Proceedings ArticleDOI
Mario Köppen, M. Teunis, Bertram Nickolay 
13 Apr 1997
14 Citations
Nessy algorithm can be characterized as an individual evolutionary algorithm, but as a neural network too.
Artificial neural network is proved to be an effective algorithm for dealing with recognition, regression and classification tasks.
It was found that artificial neural network (ANN) techniques, in general, provide better classification as compared to the pattern recognition techniques we applied earlier (M. S.
Our experiments indicate that a genetic-algorithm-based artificial neural network that maximizes the total number of correct classifications generally fares well for the binary classification problem.
Artificial neural networks, however, are able to handle classification tasks and show positive results.
The artificial neural network is an effective classification method for solving feature extraction problems.
This study shows that the artificial neural network increases the classification performance using genetic algorithm.
Neural network technique is an effective classification and prediction method.
Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis.
The artificial neural network (ANN) is a computational method based on human brain function and is efficient in recognizing previously trained patterns.

Related Questions

What are the most common machine learning techniques used in classification problems?5 answersThe most common machine learning techniques used in classification problems include logistic regression, decision trees, random forests, support vector machines (SVM), multilayer perceptrons, Naive Bayes, and neural networks. These models are widely applied in various industrial and scientific fields due to their efficiency and accuracy in predicting categorical class labels for new data. Particularly in astronomy, machine learning has been instrumental in tasks like classification, clustering, and data cleaning, where models like neural networks have shown significant utility. Additionally, recent advancements have seen the application of Bayesian neural networks for classification tasks, offering improved accuracy compared to traditional methods, albeit with challenges in predicting the certainty of predictions.
What is classification in machine learning ?5 answersClassification in machine learning refers to the task of predicting categorical class labels for new data based on observed values. It involves using various models and algorithms to draw conclusions from the data and make accurate predictions. Popular classification models include logistic regression, decision tree, random forest, support vector machine (SVM), multilayer perceptron, naive bayes, and neural networks. These models have been successfully applied to a wide range of industrial and scientific problems, including astronomy, where they have been used for tasks such as classification, clustering, and data cleaning. Machine learning techniques enable computers to automatically perform these tasks more accurately and efficiently than human operators. The accuracy and performance of classification models can be evaluated using metrics such as accuracy and F1-score.
What is classification in machine learning?5 answersClassification in machine learning refers to the process of categorizing or grouping data into different classes or categories based on their features or characteristics. It is a common method used in various fields such as vehicle noise and vibration fault diagnosis, text classification, and solving classification problems in general. Machine learning algorithms are used to analyze and learn from input data, such as images, texts, or numbers, and make predictions or decisions based on the learned patterns. The goal of classification is to automate tasks that were traditionally performed by humans, making it more efficient and cost-effective. Different machine learning techniques and algorithms are employed to solve classification problems, and the choice of algorithm depends on the specific characteristics and requirements of the data.
What are Ai algorithms?4 answersAI algorithms are a critical component of artificial intelligence systems. They are complex algorithms that are designed to capture and analyze data, identify patterns, and make decisions or predictions about real-world events. These algorithms are used in various domains such as medicine, engineering, networking, and software development. They can be trained using machine learning and deep learning techniques to improve their accuracy and efficiency. AI algorithms have been applied in medical tasks to assist physicians in diagnosis and treatment planning. They are also used in network resource management in Software Defined Networking (SDN) to optimize network performance. The goal of AI algorithms is to provide intelligent solutions by leveraging the power of data and knowledge.
What is classification algorithm ?5 answersClassification algorithm is a technique used in data mining to build a model from labelled training data for determining the class of new data. There are different types of classification algorithms such as tree-based algorithms (C4.5 decision tree, j48 decision tree), naive Bayes, and more. These algorithms have their own advantages and disadvantages depending on the characteristics of the data. The performance of classification can be measured using metrics like accuracy, precision, classification error, and kappa. Various machine learning methods have been utilized for classification, including frequency-based and geometry-based classifiers. Cellular automata have also been explored as a dynamic and discrete model for classification. Additionally, there are approaches that focus on inducing sparsity in classifiers to improve speed and storage efficiency. Active learning techniques can be applied to classification tasks to reduce wait times between user feedback iterations.
What are the state of the art video classification algorithms?3 answersVideo classification algorithms have seen significant advancements in recent years. State-of-the-art methods include deep convolutional networks (CNNs), extreme learning machine (ELM), and the bag of visual words model (BoV). CNNs have been adapted to use video frames and have yielded competitive results with hand-crafted motion features. ELM has shown good accuracy results, ranking in the top-5 alongside other algorithms like GBDT, RF, SVM, and C4.5. BoV, based on the emerging big data technology Spark, provides a fast and distributed framework for video classification. These algorithms have been used for tasks such as action recognition and have shown promising results in terms of accuracy and efficiency.

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