Learning Algorithm
A Literature Review of Personalized Learning Algorithm
... personalized learning algorithm research from two aspects of the recommendation system and data ...individualized learning algorithm, the different approaches that are applied to construct ...
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Opinion classification system using supervised learning algorithm
... supervised learning algorithm. We see that the algorithm proposed in our work out performs the previous work ...supervised learning algorithm is applied for getting these positive and ...
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Automated Skin Disease Identification using Deep Learning Algorithm
... deep learning algorithm we are able to predict as many as 20 diseases with a higher accuracy level of ...deep learning algorithms have a huge potential in the real world skin disease ...
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An Optimized Back Propagation Learning Algorithm with Adaptive Learning Rate
... propagation learning is the learning rate which values lies between ...Controlling learning rate value has become a crucial factor for neural network learning algorithm beside the ...
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An incremental learning algorithm for the hybrid RBF BP network classifier
... RAN algorithm based on an extended Kalman filter (RANEKF) is proposed in [14], which uses the extended Kalman filter algorithm instead of the least mean squares (LMS) ...sequential learning ...
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Improve the Performance of a Complex FMS with a Hybrid Machine Learning Algorithm
... machine learning technology, this problem is provided with more possibilities for the ...machine learning algorithm, inte- grating genetic algorithm and reinforcement learning ...
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DEEP LEARNING ALGORITHM USED IN ROBOTICS
... Deep learning has shown promise in significant sensing, cognition, and action problems, and even the potential to combine these normally separate functions into a single ...deep learning in robotics include ...
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Disease Prediction with Machine Learning Algorithm
... 2) Support vector machine have higher accuracy compared to the KNN algorithm; it has 85.24% accuracy. It can predict heart disease more accurately. The SVM offers very high accuracy compared to other classifiers ...
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Ontology Based Framework For Learning Algorithm
... of algorithm and programming fields, research proposed many ontologies to describe programming language elements and algorithm ...facilitate algorithm and programming learning process for ...
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Adaptive Learning for Algorithm Selection in Classification
... machine learning techniques for different data mining tasks, and has led to a proliferation of different learning ...some learning situations, then the first learner usually performs worse than the ...
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An Efficient Email Spam Detection using Support Vector Machine
... Machine Learning Algorithm Support Vector Machine is used for classification and also for regression problems where the datasets are used to train the SVM to classify any new data that it ...machine ...
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Evaluating the performance of SVM and Apriori Algorithms for Bigdata Sudha M 1, Saravana Kumar E2
... Machine Learning algorithms. Supervised Machine Learning Algorithm is One type of Machine Learning Algorithm Which is used for classifications and regressions, but SVM algorithms are ...
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Learning Theory of Randomized Kaczmarz Algorithm
... online learning algorithms associated with more general ...online learning algorithm is O(T −1/2 ) in the literature (Smale and Yao, 2005; Ying and Zhou, 2006; Smale and Zhou, 2009; Tarr´ es and Yao, ...
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Transfer Learning For Message Passing Algorithm
... new learning scenario, Transfer Learning (TL) has improves learning performance when the data can be in different feature spaces and where no correspondence between data instances in these spaces is ...
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Multi objective model selection algorithm for online sequential ultimate learning machine
... slow learning speed of ELM algorithm is taken based on the fact that the learning ability of SLFN is only related to the number of hidden layer nodes but independ- ent of the weight of the input ...
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Multi objective virtual network embedding algorithm based on Q learning and curiosity driven
... embedding algorithm based on Q-learning and curiosity- driven (Q-CD-VNE) for improving the performance of the system by optimizing conflicting objectives, namely energy saving and acceptance ...proposed ...
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Large scale Semantic Parsing via Schema Matching and Lexicon Extension
... This reduction allows us to apply standard tech- niques from each problem area, which in com- bination provide a large improvement over the purely-supervised approaches. On a dataset of 917 questions taken from 81 ...
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Multi Valued Neuron with Sigmoid Activation Function for Pattern Classification
... Multi-Valued Neuron (MVN) was proposed for pattern classification. It operates with complex-va- lued inputs, outputs, and weights, and its learning algorithm is based on error-correcting rule. The ...
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Markov Blanket based Feature Selection: A Review of Past Decade
... selection algorithm conducts a search through the space of possible combination of features and evaluates each subset by utilizing the learning algorithm of interest as a black box ...
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An Efficient Algorithm to Analyse the Cost Sensitive Measures on Datasets
... processor, learning algorithm, data model ...the algorithm are analysed and specified based on the given training data within the ...one learning algorithm is used for building a good ...
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