Inferring Task Structure From Data

نویسندگان

  • Paul E. Utgoff
  • David Jensen
  • Victor Lesser
چکیده

An algorithm is presented for fitting an expression composed of continuous and discontinuous primitive functions to real-valued data points. The data modeling problem comes from the need to infer task structure for making coordination decisions for multi-agent systems. The presence of discontinuous primitive functions requires a novel approach.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Bayesian approach to inference of population structure

Methods of inferring the population structure‎, ‎its applications in identifying disease models as well as foresighting the physical and mental situation of human beings have been finding ever-increasing importance‎. ‎In this article‎, ‎first‎, ‎motivation and significance of studying the problem of population structure is explained‎. ‎In the next section‎, ‎the applications of inference of p...

متن کامل

Methodology for Inferring Moral Priorities According to the Narrations of "Afal Tafzil"

Considering the different levels of moral values in Islam, in order to know the most important values and also to eliminate the contradiction, it is necessary to deduce from the texts of verses and hadiths. One of the most important aspects in these texts is the "structure of Tafzil". Some narrations of this structure indicate the priority of one or more values and others indicate a rule in det...

متن کامل

Inferring Social Structure of Animal Groups From Tracking Data

Inferring the social structures of animal groups from their observed behavior is a non-trivial task usually handled by direct observation. Recent advances in sensing and tracking technology have enabled the collection of dense spatial data over long periods of time automatically. The qualitative differences between sparse hand-coded data and dense tracking data necessitate a new approach to inf...

متن کامل

Diversity Regularization of Latent Variable Models: Theory, Algorithm and Applications

Latent Variable Models (LVMs) are a family of machine learning (ML) models that have been widely used in text mining, computer vision, computational biology, recommender system, to name a few. One central task in machine learning is to extract the latent knowledge and structure from observed data and LVMs elegantly fit into this task. LVMs consist of observed variables used for modeling observe...

متن کامل

Inferring multi-target QSAR models with taxonomy-based multi-task learning

BACKGROUND A plethora of studies indicate that the development of multi-target drugs is beneficial for complex diseases like cancer. Accurate QSAR models for each of the desired targets assist the optimization of a lead candidate by the prediction of affinity profiles. Often, the targets of a multi-target drug are sufficiently similar such that, in principle, knowledge can be transferred betwee...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2004