Incremental Learning of Linear Model Trees
نویسندگان
چکیده
منابع مشابه
Incremental Linear Model Trees on Big Data
The data revolution has a similar deep impact to the society as the industrial and the digital revolution. Due to the digital revolution computer were more and more integrated into daily life. Consequently, data volume was steadily increasing. It is assumed that the increase of data volume will even be, for the time being, exponential. This is due to new concepts as, e.g., social networks or th...
متن کاملIncremental Learning of Local Linear Mappings
A new incremental network model for supervised learning is proposed. The model builds up a structure of units each of which has an associated local linear mapping (LLM). Error information obtained during training is used to determine where to insert new units whose LLMs are interpolated from their neighbors. Simulation results for several classiication tasks indicate fast convergence as well as...
متن کاملUse of Decision Trees and Attributional Rules in Incremental Learning of an Intrusion Detection Model
Current intrusion detection systems are mostly based on typical data mining techniques. The growing prevalence of new network attacks represents a well-known problem which can impact the availability, confidentiality, and integrity of critical information for both individuals and enterprises. In this paper, we propose a Learnable Model for Anomaly Detection (LMAD), as an ensemble real-time intr...
متن کاملStable Decision Trees: Using Local Anarchy for Efficient Incremental Learning
This work deals with stability in incremental induction of decision trees. Stability problems arise when an induction algorithm must revise a decision tree very often and oscillations between similar concepts decrease learning speed. We introduce a heuristic and an algorithm with theoretical and experimental backing to tackle this problem.
متن کاملLearning Decision Trees with Stochastic Linear Classifiers
We consider learning decision trees in the boosting framework, where we assume that the classifiers in each internal node come from a hypothesis class HI which satisfies the weak learning assumption. In this work we consider the class of stochastic linear classifiers for HI , and derive efficient algorithms for minimizing the Gini index for this class, although the problem is non-convex. This i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Machine Learning
سال: 2005
ISSN: 0885-6125,1573-0565
DOI: 10.1007/s10994-005-1121-8