Results 281 to 290 of about 4,859,475 (319)
Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities. [PDF]
J Pathol InformGonzalez R +5 more
europepmc +1 more source
Encyclopedia of Database Systems, 2009 Linear regressors from X = R to Y = R have a limited expressive power, because they fit K affine functions to the training set. Functions that are more complex than that are approximated poorly.
Vili Podgorelec, Milan Zorman
semanticscholar +3 more sources
Economic Theory, 2005 The traditional model of sequential decision making, for instance, in extensive form games, is a tree. Most texts define a tree as a connected directed graph without loops and a distinguished node, called the root. But an abstract graph is not a domain for decision theory.
Alós-Ferrer, C., Ritzberger, K.
openaire +5 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
WIREs Computational Statistics, 2013
Decision trees trace their origins to the era of the early development of written records. This history illustrates a major strength of trees: exceptionally interpretable results which have an intuitive tree‐like display which, in turn, enhances understanding and the dissemination of results. The computational origins of decision trees—sometimes called
openaire +2 more sources
Decision trees trace their origins to the era of the early development of written records. This history illustrates a major strength of trees: exceptionally interpretable results which have an intuitive tree‐like display which, in turn, enhances understanding and the dissemination of results. The computational origins of decision trees—sometimes called
openaire +2 more sources
2009
Decision Tree Induction (DTI) is a tool to induce a classification or regression model from (usually large) datasets characterized by n objects (records), each one containing a set x of numerical or nominal attributes, and a special feature y designed as its outcome. Statisticians use the terms “predictors” to identify attributes and “response variable”
SICILIANO, ROBERTA, CONVERSANO, CLAUDIO
openaire +3 more sources
Decision Tree Induction (DTI) is a tool to induce a classification or regression model from (usually large) datasets characterized by n objects (records), each one containing a set x of numerical or nominal attributes, and a special feature y designed as its outcome. Statisticians use the terms “predictors” to identify attributes and “response variable”
SICILIANO, ROBERTA, CONVERSANO, CLAUDIO
openaire +3 more sources
An Extended Idea about Decision Trees
2019 International Conference on Computational Science and Computational Intelligence (CSCI), 2019Decision trees have been widely recognized as a data mining and machine learning methodology that receives a set of attribute values as the input and generates a Boolean decision as the output.
Feng-Jen Yang
semanticscholar +1 more source
New Splitting Criteria for Decision Trees in Stationary Data Streams
IEEE Transactions on Neural Networks and Learning Systems, 2018The most popular tools for stream data mining are based on decision trees. In previous 15 years, all designed methods, headed by the very fast decision tree algorithm, relayed on Hoeffding’s inequality and hundreds of researchers followed this scheme ...
Maciej Jaworski +2 more
semanticscholar +1 more source
Intelligent Data Analysis, 2000
Summary: The article presents the extension of a common decision tree concept to a multidimensional -- vector -- decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample.
Šprogar, Matej +4 more
openaire +3 more sources
Summary: The article presents the extension of a common decision tree concept to a multidimensional -- vector -- decision tree constructed with the help of evolutionary techniques. In contrary to the common decision tree the vector decision tree can make more than just one suggestion per input sample.
Šprogar, Matej +4 more
openaire +3 more sources
2017
This chapter provides further detail and depth on the construction of decision trees. Properties of expected value and decision trees are presented along with examples that demonstrate the use of probability and expected value of perfect and imperfect information.
openaire +2 more sources
This chapter provides further detail and depth on the construction of decision trees. Properties of expected value and decision trees are presented along with examples that demonstrate the use of probability and expected value of perfect and imperfect information.
openaire +2 more sources
2008
Decision trees are part of the decision theory and are excellent tools in the decision-making process. Majority of decision tree learning methods were developed within the last 30 years by scholars like Quinlan, Mitchell, and Breiman, just to name a few (Ozgulbas & Koyuncugil, 2006).
John Wang, Dajin Wang
openaire +1 more source
Decision trees are part of the decision theory and are excellent tools in the decision-making process. Majority of decision tree learning methods were developed within the last 30 years by scholars like Quinlan, Mitchell, and Breiman, just to name a few (Ozgulbas & Koyuncugil, 2006).
John Wang, Dajin Wang
openaire +1 more source