Inductive learning involves finding a…..
a) Consistent Hypothesis
b) Inconsistent Hypothesis
c) Regular Hypothesis
d) Irregular Hypothesis
Explanation: Inductive learning involves finding a consistent hypothesis that agrees with examples. The difficulty of the task depends on the chosen representation.
Computational learning theory analyzes the sample complexity and computational complexity of………….
a) Unsupervised Learning
b) Inductive learning
c) Forced based learning
d) Weak learning
Explanation: Computational learning theory analyzes the sample complexity and computational complexity of inductive learning. There is a tradeoff between the expressiveness of the hypothesis language and the ease of learning.
If a hypothesis says it should be positive, but in fact, it is negative, we call it……
a) A consistent hypothesis
b) A false negative hypothesis
c) A false positive hypothesis
d) A specialized hypothesis
Neural Networks are complex……with many parameters.
a) Linear Functions
b) Nonlinear Functions
c) Discrete Functions
d) Exponential Functions
Explanation: Neural networks parameters can be learned from noisy data and they have been used for thousands of applications, so it varies from problem to problem and thus use nonlinear functions.
A perceptron is a………………..
a) Feed-forward neural network
b) Backpropagation algorithm
c) Backtracking algorithm
d) Feed Forward-backward algorithm
Explanation: A perceptron is a Feed-forward neural network with no hidden units that can be representing only linear separable functions. If the data are linearly separable, a simple weight updated rule can be used to fit the data exactly.
Which of the following statement is true?
a) Not all formal languages are context-free
b) All formal languages are Context free
c) All formal languages are like natural language
d) Natural languages are context-oriented free
Explanation: Not all formal languages are context-free.
