ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL) allows the practitioner to optimize over linear combinations of kern...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Markov logic networks (MLNs) are a statistical relational model that consists of weighted firstorder clauses and generalizes first-order logic and Markov networks. The current sta...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Multi-instance learning and semi-supervised learning are different branches of machine learning. The former attempts to learn from a training set consists of labeled bags each con...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
We present a family of incremental Perceptron-like algorithms (PLAs) with margin in which both the "effective" learning rate, defined as the ratio of the learning rate t...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
We develop a mixture-based approach to robust density modeling and outlier detection for experimental multivariate data that includes measurement error information. Our model is d...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcemen...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Although semi-supervised learning has been an active area of research, its use in deployed applications is still relatively rare because the methods are often difficult to impleme...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
In this paper, we present an information-theoretic approach to learning a Mahalanobis distance function. We formulate the problem as that of minimizing the differential relative e...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representations to counteract the difficult...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...