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4 days ago · Catboost is a variant of gradient boosting that can handle both categorical and numerical features. It does not require any feature encodings techniques like One-Hot Encoder or Label Encoder to convert categorical features into numerical features. It also uses an algorithm called symmetric weighted quantile sketch (SWQS) which automatically ...
3 days ago · The Ford-Fulkerson algorithm assumes that the input will be a graph, G G, along with a source vertex, s s, and a sink vertex, t t. The graph is any representation of a weighted graph where vertices are connected by edges of specified weights. There must also be a source vertex and sink vertex to understand the beginning and end of the flow network.
4 days ago · description. This course covers basic algorithm design techniques such as divide and conquer, dynamic programming, and greedy algorithms. It concludes with a brief introduction to intractability (NP-completeness) and using linear/integer programming solvers for solving optimization problems.
2 days ago · The selection sort algorithm is as follows: Step 1: Set Min to location 0 in Step 1. Step 2: Look for the smallest element on the list. Step 3: Replace the value at location Min with a different value. Step 4: Increase Min to point to the next element. Step 5: Continue until the list is sorted.
2 days ago · One of the simplest algorithms to understand for finding Egyptian fractions is the greedy algorithm. With this algorithm, one takes a fraction \(\frac{a}{b}\) and continues to subtract off the largest fraction \(\frac{1}{n}\) until he/she is left only with a set of Egyptian fractions. Find the Egyptian fraction representation of \( \frac{8}{9} \).
2 days ago · The Dyna algorithm can be applied to various reinforcement learning tasks, including: Robotics: Enhancing the efficiency of robots in learning new tasks. Game Playing: Improving the performance of AI agents in complex games. Autonomous Driving: Enabling self-driving cars to make better decisions in dynamic environments.
5 days ago · Second, we propose a population-based iterated greedy (PBIG) algorithm, whose search space alternates sequentially from phase 1 to phase 2 to explore near-optimal solutions. Meanwhile, we present an NEH-based heuristic to generate a high-quality initial solution, and a finite skip boundary reconstruction operator to explore more promising search space and reduce computational effort.
