決策樹,Machine Learning, Tom Mitchell, McGraw Hill,第3章決策樹源碼
標簽： Learning Mitchell Machine McGraw
上傳時間： 2017-09-19
上傳用戶：小碼農lz
To describe Pattern Recognition using Machine Learning Method. It is good for one who want to learn machine learning.
標簽： Pattern recognition ML machine learning
上傳時間： 2016-04-14
上傳用戶：shishi
Pattern Recognition and Machine Learning
上傳時間： 2016-06-01
上傳用戶：who123321
Unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics
上傳時間： 2017-10-27
上傳用戶：shawnleaves
Machine learning is a broad and fascinating field. Even today, machine learning technology runs a substantial part of your life, often without you knowing it. Any plausible approach to artifi- cial intelligence must involve learning, at some level, if for no other reason than it’s hard to call a system intelligent if it cannot learn. Machine learning is also fascinating in its own right for the philo- sophical questions it raises about what it means to learn and succeed at tasks.
標簽： Learning Machine Course in
上傳時間： 2020-06-10
上傳用戶：shancjb
This book is a general introduction to machine learning that can serve as a reference book for researchers and a textbook for students. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms.
標簽： Foundations Learning Machine 2nd of
上傳時間： 2020-06-10
上傳用戶：shancjb
Machinelearninghasgreatpotentialforimprovingproducts,processesandresearch.Butcomputers usually do not explain their predictions which is a barrier to the adoption of machine learning. This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model- agnosticmethodsforinterpretingblackboxmodelslikefeatureimportanceandaccumulatedlocal effects and explaining individual predictions with Shapley values and LIME.
標簽： interpretable-machine-learning
上傳時間： 2020-06-10
上傳用戶：shancjb
Much has been written concerning the manner in which healthcare is changing, with a particular emphasis on how very large quantities of data are now being routinely collected during the routine care of patients. The use of machine learning meth- ods to turn these ever-growing quantities of data into interventions that can improve patient outcomes seems as if it should be an obvious path to take. However, the field of machine learning in healthcare is still in its infancy. This book, kindly supported by the Institution of Engineering andTechnology, aims to provide a “snap- shot” of the state of current research at the interface between machine learning and healthcare.
標簽： Technologies Healthcare Learning Machine
上傳時間： 2020-06-10
上傳用戶：shancjb
Machine learning is about designing algorithms that automatically extract valuable information from data. The emphasis here is on “automatic”, i.e., machine learning is concerned about general-purpose methodologies that can be applied to many datasets, while producing something that is mean- ingful. There are three concepts that are at the core of machine learning: data, a model, and learning.
上傳時間： 2020-06-10
上傳用戶：shancjb
* 高斯列主元素消去法求解矩陣方程AX=B,其中A是N*N的矩陣,B是N*M矩陣 * 輸入: n----方陣A的行數 * a----矩陣A * m----矩陣B的列數 * b----矩陣B * 輸出: det----矩陣A的行列式值 * a----A消元后的上三角矩陣 * b----矩陣方程的解X
上傳時間： 2015-07-26
上傳用戶：xauthu
