Sentence examples for output learning from inspiring English sources

LivesOn is a digital robot that would analyse a user's output, learning about likes and language.

This can be formulated as a structured output learning problem a quadratic programming problem with exponentially many constraints corresponding to the possible incorrect labelings.

The error function to be studied is the Cross-Entropy Error[34, 37], defined in the following expression, when a one output learning machine is considered, the desired outputs are one and zero, and F ( z ) : Z → ( 0, 1 ) (the function implemented by the system maps Z into the interval (0,1)): E = − 1 N ∑ z ∈ H 1 ln [ F ( z ) ] + ∑ z ∈ H 0 ln [ 1 − F ( z ) ] (3).

For the segmentation, we propose the use of deep structured output learning that is subsequently refined by a level set method.

Using the structured-output learning framework, we provide theoretical analyses and carry out simulations to characterize the effect of growing experimental annotations on the correctness and stability of performance estimates corresponding to different types of methods.

In supervised ML, a method tries to learn a function that maps a given input to its corresponding output for a given training data-set of known input and output values (learning from examples).

Interns and other unpaid workers are classified as employed but may produce little output while learning their trades.Still, neither answer solves the puzzle.

This observation was formalized as an inversely proportional relationship between unit costs and cumulated output called learning curve.

Artificial neural network is capable of relating the input and output parameters, learning from examples through iteration [54].

Common issues with the ANN application are to define optimum number of hidden layers, the number of neurons in these layers, functional relations between input and output parameters, learning algorithm and to avoid over-fitting (Verma and Singh 2013).

Table 3 Modular neural network construction of the second model Second model Layer Upper PEs Upper Transfer Lower PEs Lower transfer Output layer Learning rule Step size Momentum PEs Transfer Qualitatively Hidden layer.1 14 ThanAxon 14 ThanAxon – – Momentum 0.1 0.7   Hidden layer.2 14 ThanAxon 14 ThanAxon – – Momentum 0.01 0.7   Output layer – – – – 1 Linear ThanAxon Momentum 0.01 0.01