With the development of society and the in-depth advancement of higher education, postgraduates are facing increasing pressures in education, economy, work and psychology. To ensure the teaching quality of postgraduat...
详细信息
Recent theoretical results have shown that improved bounds on generalization error of classifiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, alg...
详细信息
ISBN:
(纸本)1577351894
Recent theoretical results have shown that improved bounds on generalization error of classifiers can be obtained by explicitly taking the observed margin distribution of the training data into account. Currently, algorithms used in practice do not make use of the margin distribution and are driven by optimization with respect to the points that are closest to the hyperplane. This paper enhances earlier theoretical results and derives a practical data-dependent complexity measure for learning. The new complexity measure is a function of the observed margin distribution of the data, and cab be used, as we show, as a model selection criterion. We then present the Margin Distribution Optimization (MDO) learning algorithm, that directly optimizes this complexity measures. Empirical evaluation of MDO demonstrates that it consistently outperforms SVM.
Forecasting the future demand of automotive spare parts precisely is important for car companies. The purchase of raw materials in advance, the plan of production, and the inventory levels are closely related to the p...
详细信息
Phishing is a criminal act in which a Phisher creates almost identical website connections exploiting URL Lexical characteristics to dupe unsuspecting users into exposing sensitive information such as financial data, ...
详细信息
Plant diseases have an impact on the growth of their particular species, hence early detection is critical. Many Machine learning (ML) models have been used to identify and classify plant diseases, but with developmen...
详细信息
In recent years the application of deep learning algorithms in the subdomain of audio analysis has grown rapidly, however it is a topic that can be complex for students and researchers who have a first approach and wa...
详细信息
Monkeypox is a viral illness that has been known to affect the humans. It is commonly misidentified as chickenpox due to the similarity of its rashes with chickenpox, resulting in improper treatment and further spread...
详细信息
The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a "black art" requiring expert experience, rule...
详细信息
ISBN:
(纸本)9781627480031
The use of machine learning algorithms frequently involves careful tuning of learning parameters and model hyperparameters. Unfortunately, this tuning is often a "black art" requiring expert experience, rules of thumb, or sometimes bruteforce search. There is therefore great appeal for automatic approaches that can optimize the performance of any given learning algorithm to the problem at hand. In this work, we consider this problem through the framework of Bayesian optimization, in which a learning algorithm's generalization performance is modeled as a sample from a Gaussian process (GP). We show that certain choices for the nature of the GP, such as the type of kernel and the treatment of its hyperparameters, can play a crucial role in obtaining a good optimizer that can achieve expertlevel performance. We describe new algorithms that take into account the variable cost (duration) of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation. We show that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms including latent Dirichlet allocation, structured SVMs and convolutional neural networks.
Finite automata are used to model a large variety of technical systems and form the basis of important tasks such as model-based development, early simulations and model-based diagnosis. However, such models are today...
详细信息
ISBN:
(纸本)9789898565419
Finite automata are used to model a large variety of technical systems and form the basis of important tasks such as model-based development, early simulations and model-based diagnosis. However, such models are today still mostly derived manually, in an expensive and time-consuming manner. Therefore in the past twenty years, several successful algorithms have been developed for learning various types of finite automata. These algorithms use measurements of the technical systems to automatically derive the underlying automata models. However, today users face a serious problem when looking for such model learning algorithm: Which algorithm to choose for which problem and which technical system? This papers closes this gap by comparative empirical analyses of the most popular algorithms (i) using two real-world production facilities and (ii) using artificial datasets to analyze the algorithms' convergence and scalability. Finally, based on these results, several observations for choosing an appropriate automaton learning algorithm for a specific problem are given.
The present scenario of Bangladesh Soccer Team is very much worrying. The absence of playing opportunities due to the miscoordination among the players has led the national team slip to their worst ever FIFA ranking o...
详细信息
暂无评论