NIPS
1998
14 years 27 days ago
1998
We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
NIPS
1998
14 years 27 days ago
1998
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
NIPS
1998
14 years 27 days ago
1998
Lazy learning is a memory-based technique that, once a query is received, extracts a prediction interpolating locally the neighboring examples of the query which are considered re...
NIPS
1998
14 years 27 days ago
1998
NIPS
1998
14 years 27 days ago
1998
NIPS
1998
14 years 27 days ago
1998
We introduce a semi-supervised support vector machine (S3 VM) method. Given a training set of labeled data and a working set of unlabeled data, S3 VM constructs a support vector m...
NIPS
1998
1998
Probabilistic Modeling for Face Orientation Discrimination: Learning from Labeled and Unlabeled Data
14 years 27 days ago
This paper presents probabilistic modeling methods to solve the problem of discriminating between five facial orientations with very little labeled data. Three models are explored...
NIPS
1998
14 years 27 days ago
1998
This paper describes a simple and efficient method to make template-based object classification invariant to in-plane rotations. The task is divided into two parts: orientation di...
NIPS
1998
14 years 27 days ago
1998
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
NIPS
2003
14 years 28 days ago
2003
The standard 2-norm SVM is known for its good performance in twoclass classi£cation. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advanta...