Novelty Detection techniques are conceptlearning methods that proceed by recognizing positive instances of a concept rather than differentiating between its positive and negative instances. Novelty Detection approaches consequently require very few, if any, negative training instances. This paper presents a particular Novelty Detection approach to classification that uses a Redundancy Compression and Non-Redundancy Differentiation technique based on the [Gluck & Myers, 1993] model of the hippocampus, a part of the brain critically involved in learning and memory. In particular, this approach consists of training an autoencoder to reconstruct positive input instances at the output layer and then using this autoencoder to recognize novel instances. Classification is possible, after training, because positive instances are expected to be reconstructed accurately while negative instances are not. The purpose of this paper is to compare HIPPO, the system that implements this technique, to C4.5 and feedforward neural network classification on several applications. 1

This paper describes a "bootstrapping" approach to the engineering of appropriate training-data representations for inductive learning. The central idea is to begin with an initial set of human-created features and then generate additional features that have syntactic forms that are similar to the human-engineered features. More specifically, we describe a two-stage process for the engineering of good representations for learning: first, generating by hand (usually in consultation with domain experts) an initial set of features that seem to help learning, and second, "bootstrapping" off of these features by developing and applying operators that generate new features that look syntactically like the expertbased features. Our experiments in the domain of DNA sequence identification show that an initial successful humanengineered representation for data can be expanded in this fashion to yield dramatically improved results for learning. Introduction Although most of the best-used induct...

In this paper I advocate a new model for inductive learning. Called sequential induction, this model bridges classical fixed-sample learning techniques (which are efficient but ad hoc), and worst-case approaches (which provide strong statistical guarantees but are too inefficient for practical use). According to the sequential inductive model, learning is a sequence of decisions which are informed by training data. By analyzing induction at the level of these decisions, and by utilizing the minimum data necessary to make each decision, sequential inductive techniques can provide the strong statistical guarantees of worst-case methods, but with substantially less data than those methods require. The sequential inductive model is also useful as a method for determining a sufficient sample size for inductive learning and as such, is relevant to megainduction,where the preponderance of data introduces problems of scale. The peepholing and decision-theoretic subsampling approaches of Catlet...

This paper describes a "bootstrapping" approach to the engineering of appropriate training-data representations for inductive learning. The central idea is to begin with an initial set of human-created features and then generate additional features that have syntactic forms that are similar to the humanengineered features. More specifically, we describe a process for the engineering of good representations for learning that takes as input a hand generated set of features that seem to help learning, and "bootstraps" off of these features by developing and applying operators that generate new features that look syntactically like the human-generated features. Our experiments in the domain of DNA sequence identification show that an initial successful human-engineered representation for data can be expanded in this fashion to yield dramatically improved results for learning. Although our approach is currently manual, we believe that it is expandable into a constructive induction method. 1...