A compact 3D VLSI classifier using bagging threshold network ensembles
نویسندگان
چکیده
A bagging ensemble consists of a set of classifiers trained independently and combined by a majority vote. Such a combination improves generalization performance but can require large amounts of memory and computation, a serious drawback for addressing portable real-time pattern recognition applications. We report here a compact three-dimensional (3D) multiprecision very large-scale integration (VLSI) implementation of a bagging ensemble. In our circuit, individual classifiers are decision trees implemented as threshold networks - one layer of threshold logic units (TLUs) followed by combinatorial logic functions. The hardware was fabricated using 0.7-/spl mu/m CMOS technology and packaged using MCM-V micro-packaging technology. The 3D chip implements up to 192 TLUs operating at a speed of up to 48 GCPPS and implemented in a volume of (/spl omega/ /spl times/ L /spl times/ h) = (2 /spl times/ 2 /spl times/ 0.7) cm/sup 3/. The 3D circuit features a high level of programmability and flexibility offering the possibility to make an efficient use of the hardware resources in order to reduce the power consumption. Successful operation of the 3D chip for various precisions and ensemble sizes is demonstrated through an electronic nose application.
منابع مشابه
A genetic approach for training diverse classifier ensembles
Classification is an active topic of Machine Learning. The most recent achievements in this domain suggest using ensembles of learners instead of a single classifier to improve classification accuracy. Comparisons between Bagging and Boosting show that classifier ensembles perform better when their members exhibit diversity, that is commit different errors. This paper proposes a genetic algorit...
متن کاملA 3d Model Retrieval Algorithm Based on Bp- Bagging
Aim at solving the existing problems of 3D model retrieval based on neural network, this paper proposes a new algorithm based on BP-bagging. Through bagging, the algorithm turns the weak classifier into the strong. As to feature extraction, the algorithm projections 3D model into six 2D images by six perspective points. Then transforms the images into frequency domain, gets the high dimension f...
متن کاملAn experimental study on diversity for bagging and boosting with linear classifiers
In classifier combination, it is believed that diverse ensembles have a better potential for improvement on the accuracy than nondiverse ensembles. We put this hypothesis to a test for two methods for building the ensembles: Bagging and Boosting, with two linear classifier models: the nearest mean classifier and the pseudo-Fisher linear discriminant classifier. To estimate diversity, we apply n...
متن کاملBagging and Boosting for the Nearest Mean Classifier: Effects of Sample Size on Diversity and Accuracy
In combining classifiers, it is believed that diverse ensembles perform better than non-diverse ones. In order to test this hypothesis, we study the accuracy and diversity of ensembles obtained in bagging and boosting applied to the nearest mean classifier. In our simulation study we consider two diversity measures: the Q statistic and the disagreement measure. The experiments, carried out on f...
متن کاملConstructing Diverse Classifier Ensembles using Artificial Training Examples
Ensemble methods like bagging and boosting that combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its generalization error. This paper presents a new method for generating ensembles that directly constructs diverse hypotheses using additional arti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- IEEE transactions on neural networks
دوره 14 5 شماره
صفحات -
تاریخ انتشار 2003