The training objectives of the learning object are: 1) To interpret linear classifiers in the binary case; 2) To apply the Perceptron algorithm in the binary case; 3) To know Novikoff¿s convergence theorem; and 4) To know the basic limitations of the Perceptron algorithm.https://polimedia.upv.es/visor/?id=14176b20-6fdf-11e9-a7d3-3df1cef1857dJuan Císcar, A.; Civera Saiz, J.; Sanchis Navarro, JA. (2019). Perceptron algorithm for binary classification. http://hdl.handle.net/10251/121157DE
The article explores a method for classifying elements of linearly separable sets using the Perceptr...
Dottorato di ricerca in informatica. Coordinatore Bruno Apolloni, advisor David HelmboldConsiglio Na...
Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the resul...
Linear classifiers, that is, classifiers based on linear discriminant functions, are formally intro...
One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technol...
Abstract. A perceptron is a linear threshold classifier that separates examples with a hyperplane. I...
AbstractLearning real weights for a McCulloch-Pitts neuron is equivalent to linear programming and c...
A basic neural model for Boolean computation is examined in the context of learning from examples. T...
Abstract--Since last decade, classification methods are useful in a wide range of applications. Clas...
The perceptron is essentially an adaptive linear combiner with the output quantized to ...
This is a reprint of page proofs of Chapter 12 of Perceptrons, M. Minsky and S. Papert, MIT Press 19...
I li.i DISTRIBUIYTON AVAILABILITY STATE MEK 1ýc. DISTRIBUTION COOL Approved for public release; Dist...
We consider the sample complexity of concept learning when we classify by using a fixed Boolean func...
A new training algorithm for neural networks in binary classification problems is presented. It is b...
Suppose we have N training examples. The training data are a matrix with N rows and p columns, where...
The article explores a method for classifying elements of linearly separable sets using the Perceptr...
Dottorato di ricerca in informatica. Coordinatore Bruno Apolloni, advisor David HelmboldConsiglio Na...
Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the resul...
Linear classifiers, that is, classifiers based on linear discriminant functions, are formally intro...
One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technol...
Abstract. A perceptron is a linear threshold classifier that separates examples with a hyperplane. I...
AbstractLearning real weights for a McCulloch-Pitts neuron is equivalent to linear programming and c...
A basic neural model for Boolean computation is examined in the context of learning from examples. T...
Abstract--Since last decade, classification methods are useful in a wide range of applications. Clas...
The perceptron is essentially an adaptive linear combiner with the output quantized to ...
This is a reprint of page proofs of Chapter 12 of Perceptrons, M. Minsky and S. Papert, MIT Press 19...
I li.i DISTRIBUIYTON AVAILABILITY STATE MEK 1ýc. DISTRIBUTION COOL Approved for public release; Dist...
We consider the sample complexity of concept learning when we classify by using a fixed Boolean func...
A new training algorithm for neural networks in binary classification problems is presented. It is b...
Suppose we have N training examples. The training data are a matrix with N rows and p columns, where...
The article explores a method for classifying elements of linearly separable sets using the Perceptr...
Dottorato di ricerca in informatica. Coordinatore Bruno Apolloni, advisor David HelmboldConsiglio Na...
Purpose of this work is to show that the Particle Swarm Optimization Algorithm may improve the resul...