Match the following statements to the labels 1=TRUE or 2=FALSE.

Question

BlackTom AI · Accepted Answer

Question restatement: Match the following statements to the labels 1=TRUE or 2=FALSE.

Options to evaluate:
1) When working with image data using CNN's are the better choice over RNN's. TRUE or FALSE? -------------------------
2) When working with image data using CNN's are the better choice over RNN's. TRUE or FALSE? -------------------------
3) The "Principle of Least Commitment" is something that is commonly associated with with the kNN algorithm.
4) The "Axon" of a biological neural network corresponds to the "Output" of an ANN. TRUE or FALSE? -------------------------
5) TRUE
6) FALSE

Option analysis:
Option 1: The statement suggests CNNs are the better choice for image data compared to RNNs. In typical deep learning practice, CNNs exploit spatial locality with convolutional filters and are generally preferred for image-related tasks, whereas RNNs (and variants like LSTMs) are more suited for sequential data. Therefore, this statement is true.

Option 2: This is a duplicate of Option 1. The reasoning is the same: CNNs are typically the better choice for image data, owing to their ability to capture spatial hierarchies efficiently. This statement is also true.

Option 3: The Principle of Least Commitment is a concept commonly associated with rule-based systems and expert systems, not with the kNN algorithm. kNN is a lazy, instance-based learner with no explicit model of least commitment. Therefore, this statement is false.

Option 4: In biological neurons, the axon transmits the action potential/output signal to other neurons. In artificial neural networks (ANNs), the equivalent notion of the network’s signal path corresponds to the output that a neuron emits to subsequent neurons in the next layer. Interpreted this way, the axon corresponds to the output channel of a neuron, aligning with the statement. Therefore, this statement is true.

Option 5: The standalone token "TRUE" requires evaluation in context, but if it is intended to label a statement as true, it would be true only if paired with a true statement above. Since there is no accompanying content to evaluate here, this option by itself cannot be deemed true without context. In isolation, interpret as a potential label option; its truth value depends on which prior statement it is meant to label. For analysis purposes, this option is not a substantive factual statement and is not evaluating a claim.

Option 6: The standalone token "FALSE" similarly functions as a label rather than a factual statement. Like Option 5, its truth value depends on which prior statement it is meant to label. On its own, it does not assert a claim to evaluate. Therefore, this option is not a substantive factual statement.

Summary of evaluation:
- Options 1 and 2: TRUE (with identical content; both are correct assessments about CNNs vs RNNs for image data).
- Option 3: FALSE (Principle of Least Commitment is not commonly associated with kNN).
- Option 4: TRUE (Axon corresponds to the output signal in ANN analogy).
- Options 5 and 6: Not evaluative statements by themselves; they are labels rather than factual claims to judge as true/false, so their truth value is not applicable in the same sense as the others.

Match the following statements to the labels 1=TRUE or 2=FALSE.

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