In-depth study of Machine Learning Algorithms


Many of us do not know that there is a proper list of machine learning algorithms. So here in this article, we will see some methods of using these algorithms. Through these Machine learning algorithm, you also get to know more about Artificial intelligence and designing machine learning system. These are the most important Algorithms in Machine Learning. If you are aware of these Algorithms then you can use them well to apply in almost any Data Problem.

Crowd ideation of supervised learning problems Artificial Intelligence

Crowdsourcing is an important avenue for collecting machine learning data, but crowdsourcing can go beyond simple data collection by employing the creativity and wisdom of crowd workers. Yet crowd participants are unlikely to be experts in statistics or predictive modeling, and it is not clear how well non-experts can contribute creatively to the process of machine learning. Here we study an end-to-end crowdsourcing algorithm where groups of non-expert workers propose supervised learning problems, rank and categorize those problems, and then provide data to train predictive models on those problems. Problem proposal includes and extends feature engineering because workers propose the entire problem, not only the input features but also the target variable. We show that workers without machine learning experience can collectively construct useful datasets and that predictive models can be learned on these datasets. In our experiments, the problems proposed by workers covered a broad range of topics, from politics and current events to problems capturing health behavior, demographics, and more. Workers also favored questions showing positively correlated relationships, which has interesting implications given many supervised learning methods perform as well with strong negative correlations. Proper instructions are crucial for non-experts, so we also conducted a randomized trial to understand how different instructions may influence the types of problems proposed by workers. In general, shifting the focus of machine learning tasks from designing and training individual predictive models to problem proposal allows crowdsourcers to design requirements for problems of interest and then guide workers towards contributing to the most suitable problems.

Parametrized Families of Hard Planning Problems from Phase Transitions

AAAI Conferences

There are two complementary ways to evaluate planning algorithms: performance on benchmark problems derived from real applications and analysis of performance on parametrized families of problems with known properties. Prior to this work, few means of generating parametrized families of hard planning problems were known. We generate hard planning problems from the solvable/unsolvable phase transition region of well-studied NP-complete problems that map naturally to navigation and scheduling, aspects common to many planning domains. We observe significant differences between state-of-the-art planners on these problem families, enabling us to gain insight into the relative strengths and weaknesses of these planners. Our results confirm exponential scaling of hardness with problem size, even at very small problem sizes. These families provide complementary test sets exhibiting properties not found in existing benchmarks.

Shape-Keeping Technique and Its Application to Checkmate Problem Composition

AAAI Conferences

The checkmate problem in Shogi (Japanese Chess) is a puzzle within the game itself. These puzzles have enjoyed a long play and have been the subject of centuries of analysis. The subject of this research is defining the aesthetic criteria of great Shogi problems, and finding new methods for composing interesting checkmate problems in Shogi. First we examine the results of previous studies of aesthetics in Shogi checkmate problems. For this purpose, we focus on the Proof Number Search algorithm and record the data while solving checkmate problems. We analyzed these data and we calculated the proof number related to the evaluation of the checkmate problem. Good checkmate problems have large proof numbers. Next, we present a new technique for automatic composition of checkmate problems in Shogi. This technique uses already existing checkmate problems in Shogi and develops them further. Finally, we can compose new checkmate problems which have bigger proof numbers than original ones. This work is not yet sufficient unto itself.


AAAI Conferences

Most work in heuristic search considers problems where a low cost solution is preferred (MIN problems). In this paper, we investigate the complementary setting where a solution of high reward is preferred (MAX problems). Example MAX problems include finding a longest simple path in a graph, maximal coverage, and various constraint optimization problems. We examine several popular search algorithms for MIN problems and discover the curious ways in which they misbehave on MAX problems. We propose modifications that preserve the original intentions behind the algorithms but allow them to solve MAX problems, and compare them theoretically and empirically. Interesting results include the failure of bidirectional search and close relationships between Dijkstra's algorithm, weighted A*, and depth-first search.