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- What is the fringe in the context of search algorithms?
In English, the fringe is (also) defined as the outer, marginal, or extreme part of an area, group, or sphere of activity In the context of AI search algorithms, the state (or search) space is usually represented as a graph, where nodes are states and the edges are the connections (or actions) between the corresponding states
- A* and uniform-cost search are apparently incomplete
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- Why is A* optimal if the heuristic function is admissible?
The tree search does not remember which states it has already visited, only the "fringe" of states it hasn't visited yet A graph search is a general search strategy for searching graph-structured problems, where it's possible to double back to an earlier state, like in chess (e g both players can just move their kings back and forth)
- What is the difference between tree search and graph search?
There is always a lot of confusion about this concept, because the naming is misleading, given that both tree and graph searches produce a tree (from which you can derive a path) while exploring the search space, which is usually represented as a graph
- machine learning - What is a fully convolution network? - Artificial . . .
Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations
- How does the uniform-cost search algorithm work?
Uniform Cost Search is also called the Cheapest First Search For an example and entire explanation you can directly go to this link: Udacity - Uniform Cost Search
- What are alternatives to PCA for time series data?
I have some data (20 stock price time series) and want to compare different approaches for dimensionality reduction other than PCA (I want to fit only 2 variables in my AR model)
- How is iterative deepening A* better than A*?
The iterative deepening A* search is an algorithm that can find the shortest path between a designated start node and any member of a set of goals The A* algorithm evaluates nodes by combining the
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