Andreas S. Weigend

发表

Connectionism for Music and Audition

Andreas S. Weigend, 1993, NIPS.

Forecasting Chaotic Computational Ecosystems [1]

David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, 2018, 1990 Lectures in Complex Systems.

Data Mining for Features Using Scale-Sensitive Gated Experts

Ashok N. Srivastava, Andreas S. Weigend, Renjeng Su, 1999, IEEE Trans. Pattern Anal. Mach. Intell..

Clearning Phased Array Radar Data

Andreas S. Weigend, 1997 .

The Future of Time Series: Learning and Understanding

Andreas S. Weigend, Neil Gershenfeld, A. Weigend, 1993 .

Segmenting noisy time series using scale-sensitive gated experts with applications to signal processing

Andreas S. Weigend, Renjeng Su, Ashok N. Srivastava, 1996 .

Direct Multi-Step Time Series Prediction Using TD-lambda

Andreas S. Weigend, Peter T. Kazlas, 1994, NIPS.

Analyzing customer behavior at Amazon.com

Andreas S. Weigend, 2003, KDD '03.

HIDDEN MARKOV EXPERTS

Andreas S. Weigend, Shanming Shi, 2001 .

Data Mining in Finance: Report from the Post-Nncm-96 Workshop on Teaching Computer Intensive Methods for Financial Modeling and Data Analysis

Andreas S. Weigend, A. Weigend, 1997 .

SIGIR 2003 workshop report: implicit measures of user interests and preferences

Susan T. Dumais, Thorsten Joachims, Krishna Bharat, 2003, SIGF.

Forecasting Probability Densities by Using Hidden Markov Models with Mixed States (Data Set D)

Andreas S. Weigend, A. Weigend, 2018 .

Time Series Analysis and Prediction Using Gated Experts with Application to Energy Demand Forecasts

Andreas S. Weigend, 1996, Appl. Artif. Intell..

A First Application of Independent Component Analysis to Extracting Structure from Stock Returns

Andrew D. Back, Andreas S. Weigend, A. Back, 1997, Int. J. Neural Syst..

Computing the probability density in connectionist regression

Ashok N. Srivastava, Andreas S. Weigend, 1994 .

Nonparametric selection of input variables for connectionist learning

Andreas S. Weigend, Brian Van Doren Bonnlander, A. Weigend, 1996 .

Computing second derivatives in feed-forward networks: a review

Wray L. Buntine, Andreas S. Weigend, A. Weigend, 1994, IEEE Trans. Neural Networks.

Measuring predictability using multiresolution embedding

Andreas S. Weigend, Thomas McCabe, A. Weigend, 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

Modeling Volatility Using State Space Models

Jens Timmer, Andreas S. Weigend, A. Weigend, 1997, Int. J. Neural Syst..

Predicting the Future: a Connectionist Approach

David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, 1990, Int. J. Neural Syst..

Nonlinear Trading Models Through Sharpe Ratio Maximization

Andreas S. Weigend, Mark Choey, A. Weigend, 1997, Int. J. Neural Syst..

Computing the probability density in connectionist regression

Ashok N. Srivastava, Andreas S. Weigend, A. Weigend, 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

Extracting risk-neutral densities from option prices using mixture binomial trees

Andreas S. Weigend, Christian D. Pirkner, Heinz Zimmermann, 1999, Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering (CIFEr) (IEEE Cat. No.99TH8408).

Data Mining for Financial Decision Making

Hui Wang, Andreas S. Weigend, A. Weigend, 2002, Decis. Support Syst..

Time Series Prediction: Forecasting the Future and Understanding the Past

Andreas S. Weigend, Neil A. Gershenfeld, A. Weigend, 1994 .

Predictions with Confidence Intervals ( Local Error Bars )

Andreas S. Weigend, David A. Nix, A. Weigend, 1994 .

Learning Local Error Bars for Nonlinear Regression

David A. Nix, Andreas S. Weigend, A. Weigend, 1994, NIPS.

Exploiting Hierarchy in Text Categorization

Andreas S. Weigend, Jan O. Pedersen, Erik D. Wiener, 1999, Information Retrieval.

Multi-Channel Physiological Data: Description and Analysis (Data Set B)

Andreas S. Weigend, A. Weigend, 2018 .

The Future of Time Series ( Invited Talk )

Andreas S. Weigend, A. Weigend, 1994 .

John A. Hertz, Anders S. Krogh, Richard G. Palmer, Introduction to the Theory of Neural Computation

Andreas S. Weigend, A. Weigend, 1993, Artif. Intell..

Predicting Daily Probability Distributions of S&P500 Returns

Andreas S. Weigend, Shanming Shi, A. Weigend, 1998 .

Computing portfolio risk using Gaussian mixtures and independent component analysis

Andreas S. Weigend, Heinz Zimmermann, Elion Chin, 1999, Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering (CIFEr) (IEEE Cat. No.99TH8408).

Two papers on feed-forward networks

Wray L. Buntine, Andreas S. Weigend, Wray Buntine, 1991 .

BACK-PROPAGATION, WEIGHT-ELIMINATION AND TIME SERIES PREDICTION

David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, 1991 .

The Observer-Observation Dilemma in Neuro-Forecasting: Reliable Models From Unreliable Data Through CLEARNING

Andreas S. Weigend, 1996 .

Nonlinear prediction of conditional percentiles for value-at-risk

Andreas S. Weigend, Isaac J. Chang, 1999, Proceedings of the IEEE/IAFE 1999 Conference on Computational Intelligence for Financial Engineering (CIFEr) (IEEE Cat. No.99TH8408).

Content availability, pollution and poisoning in file sharing peer-to-peer networks

Nicolas Christin, John C.-I. Chuang, Andreas S. Weigend, 2005, EC '05.

Taking time seriously: hidden Markov experts applied to financial engineering

Andreas S. Weigend, Shanming Shi, 1997, Proceedings of the IEEE/IAFE 1997 Computational Intelligence for Financial Engineering (CIFEr).

Proceedings of the 1993 Connectionist Models Summer School

David S. Touretzky, Jeffrey L. Elman, Andreas S. Weigend, 2014 .

Discovering Technical Traders in the T-bond Futures Market

Fei Chen, Andreas S. Weigend, Steven R. Waterhouse, 1998, KDD.

A neural network approach to topic spotting

Andreas S. Weigend, Jan O. Pedersen, Jan Pedersen, 1995 .

Predicting conditional probability distributions: a connectionist approach

Ashok N. Srivastava, Andreas S. Weigend, A. Weigend, 1995, Int. J. Neural Syst..

Evaluating Neural Network Predictors by Bootstrapping

Blake LeBaron, Andreas S. Weigend, B. LeBaron, 1994 .

Nonlinear gated experts for time series: discovering regimes and avoiding overfitting

Ashok N. Srivastava, Andreas S. Weigend, Morgan Mangeas, 1995, Int. J. Neural Syst..

Evaluating Neural Network Predictors by Bootstrapping ; CU-CS-725-94

Blake LeBaron, Andreas S. Weigend, B. LeBaron, 1994 .

A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series

Blake LeBaron, Andreas S. Weigend, B. LeBaron, 1996, IEEE Trans. Neural Networks.

Generalization by Weight-Elimination with Application to Forecasting

David E. Rumelhart, Andreas S. Weigend, Bernardo A. Huberman, 1990, NIPS.

Bayesian Back-Propagation

Wray L. Buntine, Andreas S. Weigend, A. Weigend, 1991, Complex Syst..

Modeling temporal structure of time series with hidden markov experts

Andreas S. Weigend, Satinder Singh Baveja, Shanming Shi, 1998 .

Weight elimination and effective network size

David E. Rumelhart, Andreas S. Weigend, D. Rumelhart, 1994, COLT 1994.

The Design of Mmm: a Model Management System for Time Series Analysis

Andreas S. Weigend, A. Weigend, 1995 .

Data Mining for Financial Decision Making

Andreas S. Weigend, Hui Wang, 2002 .

Discovering Structure in Finance Using Independent Component Analysis

Andrew D. Back, Andreas S. Weigend, A. Back, 1998 .

Decision Technologies for Financial Engineering

Andreas S. Weigend, Yaser S. Abu-Mostafa, A.-Paul N. Refenes, 1998 .

Paradigm change in prediction

Andreas S. Weigend, A. Weigend, 1994, Philosophical Transactions of the Royal Society of London. Series A: Physical and Engineering Sciences.