Above are papers related to the following paper: [Paper_ID] Paper #1114192. 	[Title] Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network [Abstract] Graphical inspection of multimodality is demonstrated using unsupervised lateral-inhibition neural networks. Three projection pursuit indices are compared on low dimensional simulated and real-world data: principal components [22], Legendre poly nomial [6] and projection pursuit network [16]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #469504. 	[Title] BCM Network develops Orientation Selectivity and Ocular Dominance in Natural Scene Environment. [Abstract] A two-eye visual environment is used in training a network of BCM neurons. We study the effect of misalignment between the synaptic density functions from the two eyes, on the formation of orientation selectivity and ocular dominance in a lateral inhibition network. The visual environment we use is composed of natural images. We show that for the BCM rule a natural image environment with binocular cortical misalignment is sufficient for producing networks with orientation selective cells and ocular dominance columns. This work is an extension of our previous single cell misalignment model (Shouval et al., 1996). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1118083. 	[Title] An integrated approach to the study of object features in visual recognition [Abstract] We propose to assess the relevance of theories of synaptic modification as models of feature extraction in human vision, by using masks derived from synaptic weight patterns to occlude parts of the stimulus images in psychophysical experiments. In the experiment reported here, we found that a mask derived from principal component analysis of object images was more effective in reducing the generalization performance of human subjects than a mask derived from another method of feature extraction (BCM), based on higher-order statistics of the images. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2695. 	[Title] Combining Exploratory Projection Pursuit And Projection Pursuit Regression With Application To Neural Networks [Abstract] We present a novel classification and regression method that combines exploratory projection pursuit (unsupervised training) with projection pursuit regression (supervised training), to yield a new family of cost/complexity penalty terms. Some improved generalization properties are demonstrated on real world problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2696. 	[Title] Objective Function Formulation of the BCM Theory of Visual Cortical Plasticity: Statistical Connections, Stability Conditions [Abstract] In this paper, we present an objective function formulation of the BCM theory of visual cortical plasticity that permits us to demonstrate the connection between the unsupervised BCM learning procedure and various statistical methods, in particular, that of Projection Pursuit. This formulation provides a general method for stability analysis of the fixed points of the theory and enables us to analyze the behavior and the evolution of the network under various visual rearing conditions. It also allows comparison with many existing unsupervised methods. This model has been shown successful in various applications such as phoneme and 3D object recognition. We thus have the striking and possibly highly significant result that a biological neuron is performing a sophisticated statistical procedure. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2698. 	[Title] Face Recognition using a Hybrid Supervised/Unsupervised Neural Network [Abstract] A system for automatic face recognition is presented. It consists of several steps; Automatic detection of the eyes and mouth is followed by a spatial normalization of the images. The classification of the normalized images is carried out by a hybrid (supervised and unsupervised) Neural Network. Two methods for reducing the overfitting a common problem in high dimensional classification schemes are presented, and the superiority of their combination is demonstrated. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #342802. 	[Title] PHONETIC CLASSIFICATION OF TIMIT SEGMENTS PREPROCESSED WITH LYON'S COCHLEAR MODEL USING A SUPERVISED/UNSUPERVISED HYBRID NEURAL NETWORK [Abstract] We report results on vowel and stop consonant recognition with tokens extracted from the TIMIT database. Our current system differs from others doing similar tasks in that we do not use any specific time normalization techniques. We use a very detailed biologically motivated input representation of the speech tokens - Lyon's cochlear model as implemented by Slaney [20]. This detailed, high dimensional representation, known as a cochleagram, is classified by either a back-propagation or by a hybrid supervised/unsupervised neural network classifier. The hybrid network is composed of a biologically motivated unsupervised network and a supervised back-propagation network. This approach produces results comparable to those obtained by others without the addition of time normalization. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #139547. 	[Title] On Centering Neural Network Weight Updates [Abstract] Technical Report IDSIA-19-97 Abstract. It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals (Schraudolph and Sejnowski, 1996). Here we generalize this notion to all factors involved in the weight update, leading us to propose centering the slope of hidden unit activation functions as well. Slope centering removes the linear component of backpropagated error; this improves credit assignment in networks with shortcut connections. Benchmark results show that this can speed up learning significantly without adversely affecting the trained network's generalization ability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123239. 	[Title] 3D Object Recognition Using Unsupervised Feature Extraction [Abstract] Intrator (1990) proposed a feature extraction method that is related to recent statistical theory (Huber, 1985; Friedman, 1987), and is based on a biologically motivated model of neuronal plasticity (Bienenstock et al., 1982). This method has been recently applied to feature extraction in the context of recognizing 3D objects from single 2D views (Intrator and Gold, 1991). Here we describe experiments designed to analyze the nature of the extracted features, and their relevance to the theory and psychophysics of object recognition. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132968. 	[Title] Local Feature Analysis: A general statistical theory for object representation [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #75695. 	[Title] Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain [Abstract] We present the information-theoretic derivation of a learning algorithm that clusters unlabelled data with linear discriminants. In contrast to methods that try to preserve information about the input patterns, we maximize the information gained from observing the output of robust binary discriminators implemented with sigmoid nodes. We derive a local weight adaptation rule via gradient ascent in this objective, demonstrate its dynamics on some simple data sets, relate our approach to previous work and suggest directions in which it may be extended. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #160705. 	[Title] Receptive Fields for Vision: from Hyperacuity to Object Recognition [Abstract] Many of the lower-level areas in the mammalian visual system are organized retinotopically, that is, as maps which preserve to a certain degree the topography of the retina. A unit that is a part of such a retinotopic map normally responds selectively to stimulation in a well-delimited part of the visual field, referred to as its receptive field (RF). Receptive fields are probably the most prominent and ubiquitous computational mechanism employed by biological information processing systems. This paper surveys some of the possible computational reasons behind the ubiquity of RFs, by discussing examples of RF-based solutions to problems in vision, from spatial acuity, through sensory coding, to object recognition. fl Weizmann Institute CS-TR 95-29, 1995; to appear in Vision, R. J. Watt, ed., MIT Press, 1996. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120197. 	[Title] On the Combination of Supervised and Unsupervised Learning  reducing the overall error measure of a classifier. [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108169. 	[Title] Extraction of Facial Features for Recognition using Neural Networks [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113035. 	[Title] Empirical Entropy Manipulation for Real-World Problems [Abstract] No finite sample is sufficient to determine the density, and therefore the entropy, of a signal directly. Some assumption about either the functional form of the density or about its smoothness is necessary. Both amount to a prior over the space of possible density functions. By far the most common approach is to assume that the density has a parametric form. By contrast we derive a differential learning rule called EMMA that optimizes entropy by way of kernel density estimation. Entropy and its derivative can then be calculated by sampling from this density estimate. The resulting parameter update rule is surprisingly simple and efficient. We will show how EMMA can be used to detect and correct corruption in magnetic resonance images (MRI). This application is beyond the scope of existing parametric entropy models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1133004. 	[Title] : Running Title: Local Multivariate Binary Processors [Abstract] We thank Sue Becker, Peter Hancock and Darragh Smyth for helpful comments on this work. The work of Dario Floreano and Bill Phillips was supported by a Network Grant from the Human Capital and Mobility Programme of the European Community. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #99025. 	[Title] Neuronal Goals: Efficient Coding and Coincidence Detection [Abstract] Barlow's seminal work on minimal entropy codes and unsupervised learning is reiterated. In particular, the need to transmit the probability of events is put in a practical neuronal framework for detecting suspicious events. A variant of the BCM learning rule [15] is presented together with some mathematical results suggesting optimal minimal entropy coding. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1108050. 	[Title] Theory of Synaptic Plasticity in Visual Cortex [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1120858. 	[Title] Three-Dimensional Object Recognition Using an Unsupervised BCM Network: The Usefulness of Distinguishing Features [Abstract] We propose an object recognition scheme based on a method for feature extraction from gray level images that corresponds to recent statistical theory, called projection pursuit, and is derived from a biologically motivated feature extracting neuron. To evaluate the performance of this method we use a set of very detailed psychophysical 3D object recognition experiments (Bulthoff and Edelman, 1992). Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #911198. 	[Title] Centering Neural Network Gradient Factors [Abstract] Technical Report IDSIA-19-97 Abstract. It has long been known that neural networks can learn faster when their input and hidden unit activities are centered about zero; recently we have extended this approach to also encompass the centering of error signals [2]. Here we generalize this notion to all factors involved in the network's gradient, leading us to propose centering the slope of hidden unit activation functions as well. Slope centering removes the linear component of backpropagated error; this improves credit assignment in networks with shortcut connections. Benchmark results show that this can speed up learning significantly without adversely affecting the trained network's generalization ability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5348. 	[Title] Feature Extraction Using an Unsupervised Neural Network [Abstract] A novel unsupervised neural network for dimensionality reduction that seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight into the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality reduction principle based solely on distinguishing features is demonstrated using a phoneme recognition experiment. The extracted features are compared with features extracted using a back-propagation network. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #256106. 	[Title] Classification of Underwater Mammals using Feature Extraction Based on Time-Frequency Analysis and BCM Theory [Abstract] Underwater mammal sound classification is demonstrated using a novel application of wavelet time/frequency decomposition and feature extraction using a BCM unsupervised network. Different feature extraction methods and different wavelet representations are studied. The system achieves outstanding classification performance even when tested with mammal sounds recorded at very different locations (from those used for training). The improved results suggest that nonlinear feature extraction from wavelet representations outperforms different linear choices of basis functions. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #5869. 	[Title] Observations on Cortical Mechanisms for Object Recognition and Learning [Abstract] This paper sketches several aspects of a hypothetical cortical architecture for visual object recognition, based on a recent computational model. The scheme relies on modules for learning from examples, such as Hyperbf-like networks, as its basic components. Such models are not intended to be precise theories of the biological circuitry but rather to capture a class of explanations we call Memory-Based Models (MBM) that contains sparse population coding, memory-based recognition and codebooks of prototypes. Unlike the sigmoidal units of some artificial neural networks, the units of MBMs are consistent with the usual description of cortical neurons as tuned to multidimensional optimal stimuli. We will describe how an example of MBM may be realized in terms of cortical circuitry and biophysical mechanisms, consistent with psychophysical and physiological data. A number of predictions, testable with physiological techniques, are made. This memo describes research done within the Center for Biological and Computational Learning in the Department of Brain and Cognitive Sciences and at the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. This research is sponsored by grants from the Office of Naval Research under contracts N00014-92-J-1879 and N00014-93-1-0385; and by a grant from the National Science Foundation under contract ASC-9217041 (this award includes funds from ARPA provided under the HPCC program). Additional support is provided by the North Atlantic Treaty Organization, ATR Audio and Visual Perception Research Laboratories, Mitsubishi Electric Corporation, Sumitomo Metal Industries, and Siemens AG. Support for the A.I. Laboratory's artificial intelligence research is provided by ARPA contract N00014-91-J-4038. Tomaso Poggio is supported by the Uncas and Helen Whitaker Chair at MIT's Whitaker College. Please summarise all the given information.

Paper #1114192 has the title `Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network  
` and the abstract `Graphical inspection of multimodality is demonstrated using unsupervised lateral-inhibition neural networks. Three projection pursuit indices are compared on low dimensional simulated and real-world data: principal components [22], Legendre poly nomial [6] and projection pursuit network [16]. 
`.
Question: Which category should Paper #1114192 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #1114192 has the title `Multimodality Exploration in Training an Unsupervised Projection Pursuit Neural Network  
` and the abstract `Graphical inspection of multimodality is demonstrated using unsupervised lateral-inhibition neural networks. Three projection pursuit indices are compared on low dimensional simulated and real-world data: principal components [22], Legendre poly nomial [6] and projection pursuit network [16]. 
`.
Question: Which category should Paper #1114192 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Neural_Networks