In the context of large space MDPs with linear value function approximation, we introduce a new approximate version of λ-Policy Iteration (Bertsekas & Ioffe, 1996), a method that generalizes Value Iteration and Policy Iteration with a parameter λ ∈ (0, 1). Our approach, called Least-Squares λ Policy Iteration, generalizes LSPI (Lagoudakis & Parr, 2003) which makes efficient use of training samp...

The paper discusses the usage of unlabeled data for Spanish Named Entity Recognition. Two techniques have been used: selftraining for detecting the entities in the text and co-training for classifying these already detected entities. We introduce a new co-training algorithm, which applies voting techniques in order to decide which unlabeled example should be added into the training set at each ...

Recurrent neural networks are often used for learning time-series data. Based on a few assumptions we model this learning task as a minimization problem of a nonlinear least-squares cost function. The special structure of the cost function allows us to build a connection to reinforcement learning. We exploit this connection and derive a convergent, policy iteration-based algorithm. Furthermore,...

Optical-processor architectures for various forms of the alternating-projection neural network are considered. Required iteration is performed by passive optical feedback. No electronics or slow optics (e.g., phase conjugators) are used in the feedback path. The processor can be taught a new training vector by viewing it only once. If the desired outputs are trained to be either +/-1, then the ...

Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. The SVDD model for normal data description builds a minimum radius hypersphere around the training data. A flexible description can be obtained by use of Kernel functions. The data description is defined by the support vectors obtained by solving quadratic optimizat...

1 Context Given a finite set of m examples z 1 ,. .. , z m and a strictly convex differen-tiable loss function ℓ(z, θ) defined on a parameter vector θ ∈ R d , we are interested in minimizing the cost function min θ C(θ) = 1 m m i=1 ℓ(z i , θ). One way to perform such a minimization is to use a stochastic gradient algorithm. Starting from some initial value θ[1], iteration t consists in picking ...

An online gradient method for BP neural networks is presented and discussed. The input training examples are permuted stochastically in each cycle of iteration. A monotonicity and a weak convergence of deterministic nature for the method are proved.

This paper presents a practical tri-training method for Chinese chunking using a small amount of labeled training data and a much larger pool of unlabeled data. We propose a novel selection method for tri-training learning in which newly labeled sentences are selected by comparing the agreements of three classifiers. In detail, in each iteration, a new sample is selected for a classifier if the...

‎In this paper we propose a new iteration process‎, ‎called the $K^{ast }$ iteration process‎, ‎for approximation of fixed‎ ‎points‎. ‎We show that our iteration process is faster than the existing well-known iteration processes using numerical examples‎. ‎Stability of the $K^{ast‎}‎$ iteration process is also discussed‎. ‎Finally we prove some weak and strong convergence theorems for Suzuki ge...